diff --git a/bob/pad/face/config/aggregated_db.py b/bob/pad/face/config/aggregated_db.py
deleted file mode 100644
index fa9d93a63e6775838229962aaa520a662b36a23a..0000000000000000000000000000000000000000
--- a/bob/pad/face/config/aggregated_db.py
+++ /dev/null
@@ -1,65 +0,0 @@
-#!/usr/bin/env python
-"""Aggregated Db is a database for face PAD experiments.
-This database aggregates the data from 3 publicly available data-sets:
-`REPLAYATTACK`_, `REPLAY-MOBILE`_ and `MSU MFSD`_.
-You can download the data for the above databases by following the corresponding
-links.
-
-The reference citation for the `REPLAYATTACK`_ is [CAM12]_.
-The reference citation for the `REPLAY-MOBILE`_ is [CBVM16]_.
-The reference citation for the `MSU MFSD`_ is [WHJ15]_.
-
-.. include:: links.rst
-"""
-
-from bob.pad.face.database import AggregatedDbPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_AGGREGATED_DB_DIRECTORIES]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".mov" # extension of the data files
-
-database = AggregatedDbPadDatabase(
- protocol='grandtest',
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- training_depends_on_protocol=True,
-)
-"""The :py:class:`bob.pad.base.database.PadDatabase` derivative with Aggregated Db
-database settings.
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[YOUR_AGGREGATED_DB_DIRECTORIES]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the Replay-Attack, Replay-Mobile
-and MSU MFSD Databases. In particular, the paths pointing to these 3 databases
-must be separated with a space. See the following note with an example of
-``[YOUR_AGGREGATED_DB_DIRECTORIES]`` entry in the ``${HOME}/.bob_bio_databases.txt`` file.
-
-.. note::
-
- [YOUR_AGGREGATED_DB_DIRECTORIES] = <PATH_TO_REPLAY_ATTACK> <PATH_TO_REPLAY_MOBILE> <PATH_TO_MSU_MFSD>
-"""
-
-protocol = 'grandtest'
-"""The default protocol to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/frame_diff_svm_aggregated_db.py b/bob/pad/face/config/frame_diff_svm_aggregated_db.py
deleted file mode 100644
index bfc018637f8a5dbad94433651879dbd9a56ac5b2..0000000000000000000000000000000000000000
--- a/bob/pad/face/config/frame_diff_svm_aggregated_db.py
+++ /dev/null
@@ -1,95 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-This file contains configurations to run Frame Differences and SVM based face PAD baseline.
-The settings of the preprocessor and extractor are tuned for the Replay-attack database.
-In the SVM algorithm the amount of training data is reduced speeding-up the training for
-large data sets, such as Aggregated PAD database.
-The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-sub_directory = 'frame_diff_svm'
-"""
-Sub-directory where results will be placed.
-
-You may change this setting using the ``--sub-directory`` command-line option
-or the attribute ``sub_directory`` in a configuration file loaded **after**
-this resource.
-"""
-
-#=======================================================================================
-# define preprocessor:
-
-from ..preprocessor import FrameDifference
-
-NUMBER_OF_FRAMES = None # process all frames
-MIN_FACE_SIZE = 50 # Minimal size of the face to consider
-
-preprocessor = FrameDifference(
- number_of_frames=NUMBER_OF_FRAMES,
- min_face_size=MIN_FACE_SIZE)
-"""
-In the preprocessing stage the frame differences are computed for both facial and non-facial/background
-regions. In this case all frames of the input video are considered, which is defined by
-``number_of_frames = None``. The frames containing faces of the size below ``min_face_size = 50`` threshold
-are discarded. Both RGB and gray-scale videos are acceptable by the preprocessor.
-The preprocessing idea is introduced in [AM11]_.
-"""
-
-#=======================================================================================
-# define extractor:
-
-from ..extractor import FrameDiffFeatures
-
-WINDOW_SIZE = 20
-OVERLAP = 0
-
-extractor = FrameDiffFeatures(window_size=WINDOW_SIZE, overlap=OVERLAP)
-"""
-In the feature extraction stage 5 features are extracted for all non-overlapping windows in
-the Frame Difference input signals. Five features are computed for each of windows in the
-facial face regions, the same is done for non-facial regions. The non-overlapping option
-is controlled by ``overlap = 0``. The length of the window is defined by ``window_size``
-argument.
-The features are introduced in the following paper: [AM11]_.
-"""
-
-#=======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import SVM
-
-MACHINE_TYPE = 'C_SVC'
-KERNEL_TYPE = 'RBF'
-N_SAMPLES = 10000
-TRAINER_GRID_SEARCH_PARAMS = {
- 'cost': [2**P for P in range(-3, 14, 2)],
- 'gamma': [2**P for P in range(-15, 0, 2)]
-}
-MEAN_STD_NORM_FLAG = True # enable mean-std normalization
-FRAME_LEVEL_SCORES_FLAG = True # one score per frame(!) in this case
-SAVE_DEBUG_DATA_FLAG = True # save the data, which might be useful for debugging
-REDUCED_TRAIN_DATA_FLAG = True # reduce the amount of training data in the final training stage
-N_TRAIN_SAMPLES = 50000 # number of training samples per class in the final SVM training stage
-
-algorithm = SVM(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- n_samples=N_SAMPLES,
- trainer_grid_search_params=TRAINER_GRID_SEARCH_PARAMS,
- mean_std_norm_flag=MEAN_STD_NORM_FLAG,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG,
- save_debug_data_flag=SAVE_DEBUG_DATA_FLAG,
- reduced_train_data_flag=REDUCED_TRAIN_DATA_FLAG,
- n_train_samples=N_TRAIN_SAMPLES)
-"""
-The SVM algorithm with RBF kernel is used to classify the data into *real* and *attack* classes.
-One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
-The grid search of SVM parameters is used to select the successful settings.
-The grid search is done on the subset of training data.
-The size of this subset is defined by ``n_samples`` parameter.
-The final training of the SVM is done on the subset of training data ``reduced_train_data_flag = True``.
-The size of the subset for the final training stage is defined by the ``n_train_samples`` argument.
-The data is also mean-std normalized, ``mean_std_norm_flag = True``.
-"""
diff --git a/bob/pad/face/config/lbp_svm_aggregated_db.py b/bob/pad/face/config/lbp_svm_aggregated_db.py
deleted file mode 100644
index 27313a4cc8f889bd997f9571034915f3d2846c8b..0000000000000000000000000000000000000000
--- a/bob/pad/face/config/lbp_svm_aggregated_db.py
+++ /dev/null
@@ -1,119 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-This file contains configurations to run LBP and SVM based face PAD baseline.
-The settings of the preprocessor and extractor are tuned for the Replay-attack database.
-In the SVM algorithm the amount of training data is reduced speeding-up the training for
-large data sets, such as Aggregated PAD database.
-The idea of the algorithm is introduced in the following paper: [CAM12]_.
-However some settings are different from the ones introduced in the paper.
-"""
-
-#=======================================================================================
-sub_directory = 'lbp_svm_aggregated_db'
-"""
-Sub-directory where results will be placed.
-
-You may change this setting using the ``--sub-directory`` command-line option
-or the attribute ``sub_directory`` in a configuration file loaded **after**
-this resource.
-"""
-
-#=======================================================================================
-# define preprocessor:
-
-from ..preprocessor import FaceCropAlign
-
-from bob.bio.video.preprocessor import Wrapper
-
-from bob.bio.video.utils import FrameSelector
-
-FACE_SIZE = 64 # The size of the resulting face
-RGB_OUTPUT_FLAG = False # Gray-scale output
-USE_FACE_ALIGNMENT = False # use annotations
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = None # use annotations
-MIN_FACE_SIZE = 50 # skip small faces
-
-_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
-_frame_selector = FrameSelector(selection_style = "all")
-
-preprocessor = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-"""
-In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
-The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces with the size
-below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
-[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``.
-"""
-
-#=======================================================================================
-# define extractor:
-
-from ..extractor import LBPHistogram
-
-from bob.bio.video.extractor import Wrapper
-
-LBPTYPE = 'uniform'
-ELBPTYPE = 'regular'
-RAD = 1
-NEIGHBORS = 8
-CIRC = False
-DTYPE = None
-
-extractor = Wrapper(LBPHistogram(
- lbptype=LBPTYPE,
- elbptype=ELBPTYPE,
- rad=RAD,
- neighbors=NEIGHBORS,
- circ=CIRC,
- dtype=DTYPE))
-"""
-In the feature extraction stage the LBP histograms are extracted from each frame of the preprocessed video.
-The parameters are similar to the ones introduced in [CAM12]_.
-"""
-
-#=======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import SVM
-
-MACHINE_TYPE = 'C_SVC'
-KERNEL_TYPE = 'RBF'
-N_SAMPLES = 10000
-TRAINER_GRID_SEARCH_PARAMS = {
- 'cost': [2**P for P in range(-3, 14, 2)],
- 'gamma': [2**P for P in range(-15, 0, 2)]
-}
-MEAN_STD_NORM_FLAG = True # enable mean-std normalization
-FRAME_LEVEL_SCORES_FLAG = True # one score per frame(!) in this case
-SAVE_DEBUG_DATA_FLAG = True # save the data, which might be useful for debugging
-REDUCED_TRAIN_DATA_FLAG = True # reduce the amount of training data in the final training stage
-N_TRAIN_SAMPLES = 50000 # number of training samples per class in the final SVM training stage
-
-algorithm = SVM(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- n_samples=N_SAMPLES,
- trainer_grid_search_params=TRAINER_GRID_SEARCH_PARAMS,
- mean_std_norm_flag=MEAN_STD_NORM_FLAG,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG,
- save_debug_data_flag=SAVE_DEBUG_DATA_FLAG,
- reduced_train_data_flag=REDUCED_TRAIN_DATA_FLAG,
- n_train_samples=N_TRAIN_SAMPLES)
-"""
-The SVM algorithm with RBF kernel is used to classify the data into *real* and *attack* classes.
-One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
-The grid search of SVM parameters is used to select the successful settings.
-The grid search is done on the subset of training data.
-The size of this subset is defined by ``n_samples`` parameter.
-The final training of the SVM is done on the subset of training data ``reduced_train_data_flag = True``.
-The size of the subset for the final training stage is defined by the ``n_train_samples`` argument.
-The data is also mean-std normalized, ``mean_std_norm_flag = True``.
-"""
diff --git a/bob/pad/face/config/maskattack.py b/bob/pad/face/config/maskattack.py
index 5f5b96803ee949bd17304c7b468a877c89072bbc..173522bcc6cb8c938ee717c9a833d03e43d862b1 100644
--- a/bob/pad/face/config/maskattack.py
+++ b/bob/pad/face/config/maskattack.py
@@ -1,20 +1,3 @@
-#!/usr/bin/env python
-"""`MSU MFSD`_ is a database for face PAD experiments.
-
-Database created at MSU, for face-PAD experiments. The public version of the database contains
-280 videos corresponding to 35 clients. The videos are grouped as 'genuine' and 'attack'.
-The attack videos have been constructed from the genuine ones,
-and consist of three kinds: print, iPad (video-replay), and iPhone (video-replay).
-Face-locations are also provided for each frame of each video, but some (6 videos) face-locations are not reliable,
-because the videos are not correctly oriented.
-The reference citation is [WHJ15]_.
-
-You can download the raw data of the `MSU MFSD`_ database by following
-the link.
-
-.. include:: links.rst
-"""
-
from bob.pad.face.database import MaskAttackPadDatabase
# Directory where the data files are stored.
@@ -29,17 +12,3 @@ database = MaskAttackPadDatabase(
original_directory=original_directory,
original_extension=original_extension,
)
-"""The :py:class:`bob.pad.base.database.PadDatabase` derivative with MSU MFSD
-database settings.
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[YOUR_MSU_MFSD_DIRECTORY]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` setting this
-value to the place where you actually installed the Replay-Mobile Database, as
-explained in the section :ref:`bob.pad.face.baselines`.
-"""
diff --git a/bob/pad/face/config/msu_mfsd.py b/bob/pad/face/config/msu_mfsd.py
deleted file mode 100644
index 7b91401aa70dca9be8529ecad8ce3433b144dec0..0000000000000000000000000000000000000000
--- a/bob/pad/face/config/msu_mfsd.py
+++ /dev/null
@@ -1,62 +0,0 @@
-#!/usr/bin/env python
-"""`MSU MFSD`_ is a database for face PAD experiments.
-
-Database created at MSU, for face-PAD experiments. The public version of the database contains
-280 videos corresponding to 35 clients. The videos are grouped as 'genuine' and 'attack'.
-The attack videos have been constructed from the genuine ones,
-and consist of three kinds: print, iPad (video-replay), and iPhone (video-replay).
-Face-locations are also provided for each frame of each video, but some (6 videos) face-locations are not reliable,
-because the videos are not correctly oriented.
-The reference citation is [WHJ15]_.
-
-You can download the raw data of the `MSU MFSD`_ database by following
-the link.
-
-.. include:: links.rst
-"""
-
-from bob.pad.face.database import MsuMfsdPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_MSU_MFSD_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = "none" # extension is not used to load the data in the HLDI of this database
-
-database = MsuMfsdPadDatabase(
- protocol='grandtest',
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- training_depends_on_protocol=True,
-)
-"""The :py:class:`bob.pad.base.database.PadDatabase` derivative with MSU MFSD
-database settings.
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[YOUR_MSU_MFSD_DIRECTORY]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` setting this
-value to the place where you actually installed the Replay-Mobile Database, as
-explained in the section :ref:`bob.pad.face.baselines`.
-"""
-
-protocol = 'grandtest'
-"""The default protocol to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/qm_one_class_svm_aggregated_db.py b/bob/pad/face/config/qm_one_class_svm_aggregated_db.py
deleted file mode 100644
index 3f010944feea8d5ebeb0c0cd05345d70aad65efd..0000000000000000000000000000000000000000
--- a/bob/pad/face/config/qm_one_class_svm_aggregated_db.py
+++ /dev/null
@@ -1,109 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-This file contains configurations to run Image Quality Measures (IQM) and one-class SVM based face PAD algorithm.
-The settings of the preprocessor and extractor are tuned for the Replay-attack database.
-In the SVM algorithm the amount of training data is reduced speeding-up the training for
-large data sets, such as Aggregated PAD database.
-The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-sub_directory = 'qm_one_class_svm_aggregated_db'
-"""
-Sub-directory where results will be placed.
-
-You may change this setting using the ``--sub-directory`` command-line option
-or the attribute ``sub_directory`` in a configuration file loaded **after**
-this resource.
-"""
-
-#=======================================================================================
-# define preprocessor:
-
-from ..preprocessor import FaceCropAlign
-
-from bob.bio.video.preprocessor import Wrapper
-
-from bob.bio.video.utils import FrameSelector
-
-FACE_SIZE = 64 # The size of the resulting face
-RGB_OUTPUT_FLAG = True # RGB output
-USE_FACE_ALIGNMENT = False # use annotations
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = None # use annotations
-MIN_FACE_SIZE = 50 # skip small faces
-
-_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
-_frame_selector = FrameSelector(selection_style = "all")
-
-preprocessor = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-"""
-In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
-The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces of the size
-below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
-[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``. The preprocessed frame is the RGB
-facial image, which is defined by ``RGB_OUTPUT_FLAG = True``.
-"""
-
-#=======================================================================================
-# define extractor:
-
-from ..extractor import ImageQualityMeasure
-
-from bob.bio.video.extractor import Wrapper
-
-GALBALLY = True
-MSU = True
-DTYPE = None
-
-extractor = Wrapper(ImageQualityMeasure(galbally=GALBALLY, msu=MSU, dtype=DTYPE))
-"""
-In the feature extraction stage the Image Quality Measures are extracted from each frame of the preprocessed RGB video.
-The features to be computed are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import SVM
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-N_SAMPLES = 50000
-TRAINER_GRID_SEARCH_PARAMS = {
- 'nu': [0.001, 0.01, 0.05, 0.1],
- 'gamma': [0.01, 0.1, 1, 10]
-}
-MEAN_STD_NORM_FLAG = True # enable mean-std normalization
-FRAME_LEVEL_SCORES_FLAG = True # one score per frame(!) in this case
-SAVE_DEBUG_DATA_FLAG = True # save the data, which might be useful for debugging
-REDUCED_TRAIN_DATA_FLAG = False # DO NOT reduce the amount of training data in the final training stage
-N_TRAIN_SAMPLES = 50000 # number of training samples per class in the final SVM training stage (NOT considered, because REDUCED_TRAIN_DATA_FLAG = False)
-
-algorithm = SVM(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- n_samples=N_SAMPLES,
- trainer_grid_search_params=TRAINER_GRID_SEARCH_PARAMS,
- mean_std_norm_flag=MEAN_STD_NORM_FLAG,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG,
- save_debug_data_flag=SAVE_DEBUG_DATA_FLAG,
- reduced_train_data_flag=REDUCED_TRAIN_DATA_FLAG,
- n_train_samples=N_TRAIN_SAMPLES)
-"""
-The one-class SVM algorithm with RBF kernel is used to classify the data into *real* and *attack* classes.
-One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
-The grid search of SVM parameters is used to select the successful settings.
-The grid search is done on the subset of training data.
-The size of this subset is defined by ``n_samples`` parameter.
-The final training of the SVM is done on all training data ``reduced_train_data_flag = False``.
-The data is also mean-std normalized, ``mean_std_norm_flag = True``.
-"""
diff --git a/bob/pad/face/config/qm_one_class_svm_cascade_aggregated_db.py b/bob/pad/face/config/qm_one_class_svm_cascade_aggregated_db.py
deleted file mode 100644
index f0f04b21dc343ac5bf958012b019120bfe029b61..0000000000000000000000000000000000000000
--- a/bob/pad/face/config/qm_one_class_svm_cascade_aggregated_db.py
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-This file contains configurations to run Image Quality Measures (IQM) and SVM based face PAD baseline.
-The settings of the preprocessor and extractor are tuned for the Replay-attack database.
-In the SVM algorithm the amount of training data is reduced speeding-up the training for
-large data sets, such as Aggregated PAD database.
-The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-sub_directory = 'qm_svm_aggregated_db'
-"""
-Sub-directory where results will be placed.
-
-You may change this setting using the ``--sub-directory`` command-line option
-or the attribute ``sub_directory`` in a configuration file loaded **after**
-this resource.
-"""
-
-#=======================================================================================
-# define preprocessor:
-
-from ..preprocessor import FaceCropAlign
-
-from bob.bio.video.preprocessor import Wrapper
-
-from bob.bio.video.utils import FrameSelector
-
-FACE_SIZE = 64 # The size of the resulting face
-RGB_OUTPUT_FLAG = True # RGB output
-USE_FACE_ALIGNMENT = False # use annotations
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = None # use annotations
-MIN_FACE_SIZE = 50 # skip small faces
-
-_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
-_frame_selector = FrameSelector(selection_style = "all")
-
-preprocessor = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-"""
-In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
-The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces of the size
-below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
-[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``. The preprocessed frame is the RGB
-facial image, which is defined by ``RGB_OUTPUT_FLAG = True``.
-"""
-
-#=======================================================================================
-# define extractor:
-
-from ..extractor import ImageQualityMeasure
-
-from bob.bio.video.extractor import Wrapper
-
-GALBALLY = True
-MSU = True
-DTYPE = None
-
-extractor = Wrapper(ImageQualityMeasure(galbally=GALBALLY, msu=MSU, dtype=DTYPE))
-"""
-In the feature extraction stage the Image Quality Measures are extracted from each frame of the preprocessed RGB video.
-The features to be computed are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import SVMCascadePCA
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.5}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-"""
-The cascade of one-class SVMs with RBF kernel is used to classify the data into *real* and *attack* classes.
-One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
-A single SVM in the cascade is trained using two features ``N = 2``.
-The positive scores produced by the cascade are reduced by multiplying them with a constant
-``pos_scores_slope = 0.01``.
-"""
diff --git a/bob/pad/face/config/qm_svm_aggregated_db.py b/bob/pad/face/config/qm_svm_aggregated_db.py
deleted file mode 100644
index 8fafbbd5f277116557f4ec22517acb9832e6751b..0000000000000000000000000000000000000000
--- a/bob/pad/face/config/qm_svm_aggregated_db.py
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-This file contains configurations to run Image Quality Measures (IQM) and SVM based face PAD baseline.
-The settings of the preprocessor and extractor are tuned for the Replay-attack database.
-In the SVM algorithm the amount of training data is reduced speeding-up the training for
-large data sets, such as Aggregated PAD database.
-The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-sub_directory = 'qm_svm_aggregated_db'
-"""
-Sub-directory where results will be placed.
-
-You may change this setting using the ``--sub-directory`` command-line option
-or the attribute ``sub_directory`` in a configuration file loaded **after**
-this resource.
-"""
-
-#=======================================================================================
-# define preprocessor:
-
-from ..preprocessor import FaceCropAlign
-
-from bob.bio.video.preprocessor import Wrapper
-
-from bob.bio.video.utils import FrameSelector
-
-FACE_SIZE = 64 # The size of the resulting face
-RGB_OUTPUT_FLAG = True # RGB output
-USE_FACE_ALIGNMENT = False # use annotations
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = None # use annotations
-MIN_FACE_SIZE = 50 # skip small faces
-
-_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
-_frame_selector = FrameSelector(selection_style = "all")
-
-preprocessor = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-"""
-In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
-The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces of the size
-below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
-[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``. The preprocessed frame is the RGB
-facial image, which is defined by ``RGB_OUTPUT_FLAG = True``.
-"""
-
-#=======================================================================================
-# define extractor:
-
-from ..extractor import ImageQualityMeasure
-
-from bob.bio.video.extractor import Wrapper
-
-GALBALLY = True
-MSU = True
-DTYPE = None
-
-extractor = Wrapper(ImageQualityMeasure(galbally=GALBALLY, msu=MSU, dtype=DTYPE))
-"""
-In the feature extraction stage the Image Quality Measures are extracted from each frame of the preprocessed RGB video.
-The features to be computed are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import SVM
-
-MACHINE_TYPE = 'C_SVC'
-KERNEL_TYPE = 'RBF'
-N_SAMPLES = 10000
-TRAINER_GRID_SEARCH_PARAMS = {
- 'cost': [2**P for P in range(-3, 14, 2)],
- 'gamma': [2**P for P in range(-15, 0, 2)]
-}
-MEAN_STD_NORM_FLAG = True # enable mean-std normalization
-FRAME_LEVEL_SCORES_FLAG = True # one score per frame(!) in this case
-SAVE_DEBUG_DATA_FLAG = True # save the data, which might be useful for debugging
-REDUCED_TRAIN_DATA_FLAG = True # reduce the amount of training data in the final training stage
-N_TRAIN_SAMPLES = 50000 # number of training samples per class in the final SVM training stage
-
-algorithm = SVM(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- n_samples=N_SAMPLES,
- trainer_grid_search_params=TRAINER_GRID_SEARCH_PARAMS,
- mean_std_norm_flag=MEAN_STD_NORM_FLAG,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG,
- save_debug_data_flag=SAVE_DEBUG_DATA_FLAG,
- reduced_train_data_flag=REDUCED_TRAIN_DATA_FLAG,
- n_train_samples=N_TRAIN_SAMPLES)
-"""
-The SVM algorithm with RBF kernel is used to classify the data into *real* and *attack* classes.
-One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
-The grid search of SVM parameters is used to select the successful settings.
-The grid search is done on the subset of training data.
-The size of this subset is defined by ``n_samples`` parameter.
-The final training of the SVM is done on the subset of training data ``reduced_train_data_flag = True``.
-The size of the subset for the final training stage is defined by the ``n_train_samples`` argument.
-The data is also mean-std normalized, ``mean_std_norm_flag = True``.
-"""
diff --git a/bob/pad/face/database/__init__.py b/bob/pad/face/database/__init__.py
index db9961d0bd8f5d040feec580293c21348af4b2d9..63f053760b5a551ef3d732c63270109fd1e97e54 100644
--- a/bob/pad/face/database/__init__.py
+++ b/bob/pad/face/database/__init__.py
@@ -1,8 +1,6 @@
from .database import VideoPadFile
from .replay import ReplayPadDatabase
from .replay_mobile import ReplayMobilePadDatabase
-from .msu_mfsd import MsuMfsdPadDatabase
-from .aggregated_db import AggregatedDbPadDatabase
from .mifs import MIFSPadDatabase
from .batl import BatlPadDatabase
from .celeb_a import CELEBAPadDatabase
@@ -32,8 +30,6 @@ __appropriate__(
VideoPadFile,
ReplayPadDatabase,
ReplayMobilePadDatabase,
- MsuMfsdPadDatabase,
- AggregatedDbPadDatabase,
MIFSPadDatabase,
BatlPadDatabase,
CELEBAPadDatabase,
diff --git a/bob/pad/face/database/aggregated_db.py b/bob/pad/face/database/aggregated_db.py
deleted file mode 100644
index 6a30de5e72864cbc6d00590e3f927eb286ae4187..0000000000000000000000000000000000000000
--- a/bob/pad/face/database/aggregated_db.py
+++ /dev/null
@@ -1,1014 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-
-# =============================================================================
-from bob.pad.face.database import VideoPadFile
-
-from bob.pad.base.database import PadDatabase
-
-# Import HLDI for the databases to aggregate:
-from bob.pad.face.database import replay as replay_hldi
-
-from bob.pad.face.database import replay_mobile as replay_mobile_hldi
-
-from bob.pad.face.database import msu_mfsd as msu_mfsd_hldi
-
-from bob.bio.video.database.mobio import MobioBioFile
-
-from bob.bio.video import FrameSelector, FrameContainer
-
-import numpy as np
-
-# =============================================================================
-class AggregatedDbPadFile(VideoPadFile):
- """
- A high level implementation of the File class for the Aggregated Database
- uniting 4 databases: REPLAY-ATTACK, REPLAY-MOBILE, MSU MFSD and Mobio.
- """
-
- def __init__(self, f):
- """
- **Parameters:**
-
- ``f`` : :py:class:`object`
- An instance of the File class defined in the low level db interface
- of Replay-Attack, or Replay-Mobile, or MSU MFSD, or Mobio database,
- respectively:
- in the bob.db.replay.models.py file or
- in the bob.db.replaymobile.models.py file or
- in the bob.db.msu_mfsd_mod.models.py file or
- in the bob.db.mobio.models.py file.
- """
-
- self.f = f
- # this f is actually an instance of the File class that is defined in
- # bob.db.<database_name>.models and the PadFile class here needs
- # client_id, path, attack_type, file_id for initialization. We have to
- # convert information here and provide them to PadFile. attack_type is a
- # little tricky to get here. Based on the documentation of PadFile:
- # In cased of a spoofed data, this parameter should indicate what kind of spoofed attack it is.
- # The default None value is interpreted that the PadFile is a genuine or real sample.
-
- import bob.db.mobio
-
- if isinstance(f, bob.db.mobio.models.File
- ): # MOBIO files doen't have is_real() method
-
- attack_type = None
-
- else:
-
- if f.is_real():
- attack_type = None
- else:
- attack_type = 'attack'
- # attack_type is a string and I decided to make it like this for this
- # particular database. You can do whatever you want for your own database.
-
- file_path = self.encode_file_path(f)
-
- file_id = self.encode_file_id(f)
-
- super(AggregatedDbPadFile, self).__init__(
- client_id=f.client_id,
- path=file_path,
- attack_type=attack_type,
- file_id=file_id)
-
- # =========================================================================
- def encode_file_id(self, f, n=2000):
- """
- Return a modified version of the ``f.id`` ensuring uniqueness of the ids
- across all databases.
-
- **Parameters:**
-
- ``f`` : :py:class:`object`
- An instance of the File class defined in the low level db interface
- of Replay-Attack, or Replay-Mobile, or MSU MFSD, or Mobio database,
- respectively:
- in the bob.db.replay.models.py file or
- in the bob.db.replaymobile.models.py file or
- in the bob.db.msu_mfsd_mod.models.py file or
- in the bob.db.mobio.models.py file.
-
- ``n`` : :py:class:`int`
- An offset to be added to the file id for different databases is defined
- as follows: offset = k*n, where k is the database number,
- k = 0,1,2 in our case. Default: 2000.
-
- **Returns:**
-
- ``file_id`` : :py:class:`int`
- A modified version of the file id, which is now unigue accross
- all databases.
- """
-
- import bob.db.replay
- import bob.db.replaymobile
- import bob.db.msu_mfsd_mod
- import bob.db.mobio
-
- if isinstance(
- f, bob.db.replay.models.File
- ): # check if instance of File class of LLDI of Replay-Attack
-
- file_id = f.id
-
- if isinstance(
- f, bob.db.replaymobile.models.File
- ): # check if instance of File class of LLDI of Replay-Mobile
-
- file_id = np.int(f.id + n)
-
- if isinstance(f, bob.db.msu_mfsd_mod.models.File
- ): # check if instance of File class of LLDI of MSU MFSD
-
- file_id = np.int(f.id + 2 * n)
-
- if isinstance(f, bob.db.mobio.models.File
- ): # check if instance of File class of LLDI of Mobio
-
- file_id = np.int(f.id + 3 * n)
-
- return file_id
-
- # =========================================================================
- def encode_file_path(self, f):
- """
- Append the name of the database to the end of the file path separated
- with "_".
-
- **Parameters:**
-
- ``f`` : :py:class:`object`
- An instance of the File class defined in the low level db interface
- of Replay-Attack, or Replay-Mobile, or MSU MFSD, or Mobio database,
- respectively:
- in the bob.db.replay.models.py file or
- in the bob.db.replaymobile.models.py file or
- in the bob.db.msu_mfsd_mod.models.py file or
- in the bob.db.mobio.models.py file.
-
- **Returns:**
-
- ``file_path`` : :py:class:`str`
- Modified path to the file, with database name appended to the end
- separated with "_".
- """
-
- import bob.db.replay
- import bob.db.replaymobile
- import bob.db.msu_mfsd_mod
- import bob.db.mobio
-
- if isinstance(
- f, bob.db.replay.models.File
- ): # check if instance of File class of LLDI of Replay-Attack
-
- file_path = '_'.join([f.path, 'replay'])
-
- if isinstance(
- f, bob.db.replaymobile.models.File
- ): # check if instance of File class of LLDI of Replay-Mobile
-
- file_path = '_'.join([f.path, 'replaymobile'])
-
- if isinstance(f, bob.db.msu_mfsd_mod.models.File
- ): # check if instance of File class of LLDI of MSU MFSD
-
- file_path = '_'.join([f.path, 'msu_mfsd_mod'])
-
- if isinstance(f, bob.db.mobio.models.File
- ): # check if instance of File class of LLDI of Mobio
-
- file_path = '_'.join([f.path, 'mobio'])
-
- return file_path
-
- # =========================================================================
- def load(self, directory=None, extension='.mov',
- frame_selector=FrameSelector(selection_style='all')):
- """
- Overridden version of the load method defined in the ``VideoPadFile``.
-
- **Parameters:**
-
- ``directory`` : :py:class:`str`
- String containing the paths to all databases used in this aggregated
- database. The paths are separated with a space.
-
- ``extension`` : :py:class:`str`
- Extension of the video files in the REPLAY-ATTACK and REPLAY-MOBILE
- databases. The extension of files in MSU MFSD is not taken into account
- in the HighLevel DB Interface of MSU MFSD. Default: '.mov'.
-
- **Returns:**
-
- ``video_data`` : FrameContainer
- Video data stored in the FrameContainer, see ``bob.bio.video.utils.FrameContainer``
- for further details.
- """
-
- import bob.db.replay
- import bob.db.replaymobile
- import bob.db.msu_mfsd_mod
- import bob.db.mobio
-
- directories = directory.split(" ")
-
- if isinstance(
- self.f, bob.db.replay.models.File
- ): # check if instance of File class of LLDI of Replay-Attack
-
- db_pad_file = replay_hldi.ReplayPadFile(
- self.f) # replay_hldi is HLDI of Replay-Attack
-
- directory = directories[0]
-
- if isinstance(
- self.f, bob.db.replaymobile.models.File
- ): # check if instance of File class of LLDI of Replay-Mobile
-
- db_pad_file = replay_mobile_hldi.ReplayMobilePadFile(
- self.f) # replay_mobile_hldi is HLDI of Replay-Mobile
-
- directory = directories[1]
-
- if isinstance(self.f, bob.db.msu_mfsd_mod.models.File
- ): # check if instance of File class of LLDI of MSU MFSD
-
- db_pad_file = msu_mfsd_hldi.MsuMfsdPadFile(
- self.f) # msu_mfsd_hldi is HLDI of MSU MFSD
-
- directory = directories[2]
-
- if isinstance(self.f, bob.db.mobio.models.File
- ): # check if instance of File class of LLDI of Mobio
-
- db_pad_file = MobioBioFile(
- self.f) # msu_mfsd_hldi is HLDI of MSU MFSD
-
- directory = directories[3]
-
- if isinstance(db_pad_file, bob.bio.video.database.mobio.MobioBioFile):
-
- video_data = db_pad_file.load(
- directory=directory,
- extension='.mp4',
- frame_selector=frame_selector)
-
- else:
-
- video_data = db_pad_file.load(
- directory=directory,
- extension=extension,
- frame_selector=frame_selector)
-
- return video_data # video data
-
-
-# =============================================================================
-class AggregatedDbPadDatabase(PadDatabase):
- """
- A high level implementation of the Database class for the Aggregated Database
- uniting 3 databases: REPLAY-ATTACK, REPLAY-MOBILE and MSU MFSD. Currently this
- database supports 5 protocols, which are listed in the ``available_protocols``
- argument of this class.
-
- Available protocols are:
-
- 1. "grandtest" - this protocol is using all the data available in the
- databases Replay-Attack, Replay-Mobile, MSU MFSD.
-
- 2. "photo-photo-video" - this protocol is used to test the system on
- unseen types of attacks. In this case the attacks are splitted
- as follows:
- 'train' set - only **photo** attacks are used for training,
- 'dev' set - only **photo** attacks are used for threshold tuning,
- 'eval' set - only **video** attacks are used in final evaluation.
- In this case the final performance is estimated on previously
- unseen **video** attacks.
-
- 3. "video-video-photo" - this protocol is used to test the system on
- unseen types of attacks. In this case the attacks are splitted
- as follows:
- 'train' set - only **video** attacks are used for training,
- 'dev' set - only **video** attacks are used for threshold tuning,
- 'eval' set - only **photo** attacks are used in final evaluation.
- In this case the final performance is estimated on previously
- unseen **photo** attacks.
-
- 4. "grandtest-mobio" - this protocol is using all the data available in the
- databases Replay-Attack, Replay-Mobile, MSU MFSD plus some additional data
- from MOBIO dataset is used in the training set.
-
- 5. "grandtest-train-eval" - this protocol is using all the data available
- in the databases Replay-Attack, Replay-Mobile, MSU MFSD. Only two gropus
- 'train' and 'eval' are available in this protocol. The 'dev' set is
- concatenated to the training data. When requesting 'dev' set, the
- data of the 'eval' set is returned.
-
- 6. "grandtest-train-eval-<num_train_samples>" -
- this protocol is using all the data available in the databases
- Replay-Attack, Replay-Mobile, MSU MFSD. Only two gropus
- 'train' and 'eval' are available in this protocol. The 'dev' set is
- concatenated to the training data. When requesting 'dev' set, the
- data of the 'eval' set is returned.
- MOREOVER, in this protocol you can specify the number of training samples
- <num_train_samples>, which will be uniformly selected for each database
- (Replay-Attack, Replay-Mobile, MSU MFSD) used in the Aggregated DB.
- For example, in the protocol "grandtest-train-eval-5", 5 training samples
- will be selected for Replay-Attack, 5 for Replay-Mobile, and 5 for
- MSU MFSD. The total number of training samples is 15 in this case.
- """
-
- def __init__(
- self,
- protocol='grandtest', # grandtest is the default protocol for this database
- original_directory=None,
- original_extension=None,
- **kwargs):
- """
- **Parameters:**
-
- ``protocol`` : :py:class:`str` or ``None``
- The name of the protocol that defines the default experimental setup
- for this database. Default: 'grandtest'.
-
- ``original_directory`` : :py:class:`str`
- String containing the paths to all databases used in this aggregated
- database. The paths are separated with a space. Default: None.
-
- ``original_extension`` : :py:class:`str`
- Extension of the video files in the REPLAY-ATTACK and REPLAY-MOBILE
- databases. The extension of files in MSU MFSD is not taken into account
- in the HighLevel DB Interface of MSU MFSD. Default: None.
-
- ``kwargs``
- The arguments of the :py:class:`bob.bio.base.database.BioDatabase`
- base class constructor.
- """
-
- # Import LLDI for all databases:
- import bob.db.replay
- import bob.db.replaymobile
- import bob.db.msu_mfsd_mod
- import bob.db.mobio
-
- self.replay_db = bob.db.replay.Database()
- self.replaymobile_db = bob.db.replaymobile.Database()
- self.msu_mfsd_db = bob.db.msu_mfsd_mod.Database()
- self.mobio = bob.db.mobio.Database()
-
- # Since the high level API expects different group names than what the low
- # level API offers, you need to convert them when necessary
- self.low_level_group_names = (
- 'train', 'devel',
- 'test') # group names in the low-level database interface
- self.high_level_group_names = (
- 'train', 'dev',
- 'eval') # names are expected to be like that in objects() function
-
- # A list of available protocols:
- self.available_protocols = [
- 'grandtest', 'photo-photo-video', 'video-video-photo',
- 'grandtest-mobio', 'grandtest-train-eval',
- 'grandtest-train-eval-<num_train_samples>']
-
- # Always use super to call parent class methods.
- super(AggregatedDbPadDatabase, self).__init__(
- name='aggregated_db',
- protocol=protocol,
- original_directory=original_directory,
- original_extension=original_extension,
- **kwargs)
-
- # =========================================================================
- def get_mobio_files_given_single_group(self, groups=None, purposes=None):
- """
- Get a list of files for the MOBIO database. All files are bona-fide
- samples and used only for training. Thus, a non-empty list is returned
- only when groups='train' and purposes='real'.
- Only one file per client is selected. The files collected in Idiap are
- excluded from training set to make sure identities in 'train' set don't
- overlap with 'devel' and 'test' sets.
-
- **Parameters:**
-
- ``groups`` : :py:class:`str`
- The group of which the clients should be returned.
- One element of ('train', 'devel', 'test').
-
- ``purposes`` : :py:class:`str`
- OR a list of strings.
- The purposes for which File objects should be retrieved.
- Usually it is either 'real' or 'attack'.
-
- **Returns:**
-
- ``mobio_files`` : [File]
- A list of files, as defined in the low level interface of the MOBIO
- database.
- """
-
- mobio_files = []
-
- if (groups is not None) and ('train' in groups) and (
- purposes is not None) and ('real' in purposes):
-
- files_mobio = self.mobio.all_files()
-
- metadata = []
-
- for f in files_mobio:
-
- metadata.append((f.client_id))
-
- metadata_set = list(
- set(metadata)) # metadata_set is a list of unique client ids
-
- for f in files_mobio:
-
- metadata = (f.client_id)
-
- if metadata in metadata_set: # only one video per client id is selected
-
- metadata_set.remove(metadata)
-
- if "idiap" not in f.path:
- # videos collected at idiap are excluded to make sure identities in train set dont overlap with dev and test sets.
- mobio_files.append(f)
-
- return mobio_files
-
- # =========================================================================
- def uniform_select_list_elements(self, data, n_samples):
- """
- Uniformly select N elements from the input data list.
-
- **Parameters:**
-
- ``data`` : []
- Input list to select elements from.
-
- ``n_samples`` : :py:class:`int`
- The number of samples to be selected uniformly from the input list.
-
- **Returns:**
-
- ``selected_data`` : []
- Selected subset of elements.
- """
-
- if len(data) <= n_samples:
-
- selected_data = data
-
- else:
-
- uniform_step = len(data) / np.float(n_samples + 1)
-
- idxs = [int(np.round(uniform_step * (x + 1)))
- for x in range(n_samples)]
-
- selected_data = [data[idx] for idx in idxs]
-
- return selected_data
-
- # =========================================================================
- def get_files_given_single_group(self,
- groups=None,
- protocol=None,
- purposes=None,
- model_ids=None,
- **kwargs):
- """
- This function returns 4 lists of files for Raplay-Attack, Replay-Mobile,
- MSU MFSD and MOBIO databases, which fulfill the given restrictions. This
- function for the groups parameter accepts a single string ONLY, which
- determines the low level name of the group, see ``low_level_group_names``
- argument of this class for available options.
-
- **Parameters:**
-
- ``groups`` : :py:class:`str`
- The group of which the clients should be returned.
- One element of ('train', 'devel', 'test').
-
- ``protocol`` : :py:class:`str`
- The protocol for which the clients should be retrieved.
- Available options are defined in the ``available_protocols`` argument
- of the class. So far the following protocols are available:
-
- 1. "grandtest" - this protocol is using all the data available in the
- databases Replay-Attack, Replay-Mobile, MSU MFSD.
-
- 2. "photo-photo-video" - this protocol is used to test the system on
- unseen types of attacks. In this case the attacks are splitted
- as follows:
- 'train' set - only **photo** attacks are used for training,
- 'dev' set - only **photo** attacks are used for threshold tuning,
- 'eval' set - only **video** attacks are used in final evaluation.
- In this case the final performance is estimated on previously
- unseen **video** attacks.
-
- 3. "video-video-photo" - this protocol is used to test the system on
- unseen types of attacks. In this case the attacks are splitted
- as follows:
- 'train' set - only **video** attacks are used for training,
- 'dev' set - only **video** attacks are used for threshold tuning,
- 'eval' set - only **photo** attacks are used in final evaluation.
- In this case the final performance is estimated on previously
- unseen **photo** attacks.
-
- 4. "grandtest-mobio" - this protocol is using all the data available in the
- databases Replay-Attack, Replay-Mobile, MSU MFSD plus some additional data
- from MOBIO dataset is used in the training set.
-
- 5. "grandtest-train-eval" - this protocol is using all the data available
- in the databases Replay-Attack, Replay-Mobile, MSU MFSD. Only two gropus
- 'train' and 'test' are available in this protocol. The 'devel' set is
- concatenated to the training data. When requesting 'devel' set, the
- data of the 'test' set is returned.
-
- 6. "grandtest-train-eval-<num_train_samples>" -
- this protocol is using all the data available in the databases
- Replay-Attack, Replay-Mobile, MSU MFSD. Only two gropus
- 'train' and 'eval' are available in this protocol. The 'dev' set is
- concatenated to the training data. When requesting 'dev' set, the
- data of the 'eval' set is returned.
- MOREOVER, in this protocol you can specify the number of training samples
- <num_train_samples>, which will be uniformly selected for each database
- (Replay-Attack, Replay-Mobile, MSU MFSD) used in the Aggregated DB.
- For example, in the protocol "grandtest-train-eval-5", 5 training samples
- will be selected for Replay-Attack, 5 for Replay-Mobile, and 5 for
- MSU MFSD. The total number of training samples is 15 in this case.
-
- ``purposes`` : :py:class:`str`
- OR a list of strings.
- The purposes for which File objects should be retrieved.
- Usually it is either 'real' or 'attack'.
-
- ``model_ids``
- This parameter is not supported in PAD databases yet
-
- **Returns:**
-
- ``replay_files`` : [File]
- A list of files corresponding to Replay-Attack database.
-
- ``replaymobile_files`` : [File]
- A list of files corresponding to Replay-Mobile database.
-
- ``msu_mfsd_files`` : [File]
- A list of files corresponding to MSU MFSD database.
-
- ``mobio_files`` : [File]
- A list of files corresponding to MOBIO database or an empty list.
- """
-
- if protocol == 'grandtest' or protocol is None or groups is None:
-
- replay_files = self.replay_db.objects(
- protocol=protocol, groups=groups, cls=purposes, **kwargs)
-
- replaymobile_files = self.replaymobile_db.objects(
- protocol=protocol, groups=groups, cls=purposes, **kwargs)
-
- msu_mfsd_files = self.msu_mfsd_db.objects(
- group=groups, cls=purposes, **kwargs)
-
- if protocol == 'photo-photo-video':
-
- # the group names are low-level here: ('train', 'devel', 'test')
- if groups == 'train' or groups == 'devel':
-
- replay_files = self.replay_db.objects(
- protocol='photo', groups=groups, cls=purposes, **kwargs)
-
- replaymobile_files = self.replaymobile_db.objects(
- protocol='grandtest',
- groups=groups,
- cls=purposes,
- sample_type='photo',
- **kwargs)
-
- msu_mfsd_files = self.msu_mfsd_db.objects(
- group=groups,
- cls=purposes,
- instrument=('print', ''),
- **kwargs)
-
- if groups == 'test':
-
- replay_files = self.replay_db.objects(
- protocol='video', groups=groups, cls=purposes, **kwargs)
-
- replaymobile_files = self.replaymobile_db.objects(
- protocol='grandtest',
- groups=groups,
- cls=purposes,
- sample_type='video',
- **kwargs)
-
- msu_mfsd_files = self.msu_mfsd_db.objects(
- group=groups,
- cls=purposes,
- instrument=('video_hd', 'video_mobile', ''),
- **kwargs)
-
- if protocol == 'video-video-photo':
-
- # the group names are low-level here: ('train', 'devel', 'test')
- if groups == 'train' or groups == 'devel':
-
- replay_files = self.replay_db.objects(
- protocol='video', groups=groups, cls=purposes, **kwargs)
-
- replaymobile_files = self.replaymobile_db.objects(
- protocol='grandtest',
- groups=groups,
- cls=purposes,
- sample_type='video',
- **kwargs)
-
- msu_mfsd_files = self.msu_mfsd_db.objects(
- group=groups,
- cls=purposes,
- instrument=('video_hd', 'video_mobile', ''),
- **kwargs)
-
- if groups == 'test':
-
- replay_files = self.replay_db.objects(
- protocol='photo', groups=groups, cls=purposes, **kwargs)
-
- replaymobile_files = self.replaymobile_db.objects(
- protocol='grandtest',
- groups=groups,
- cls=purposes,
- sample_type='photo',
- **kwargs)
-
- msu_mfsd_files = self.msu_mfsd_db.objects(
- group=groups,
- cls=purposes,
- instrument=('print', ''),
- **kwargs)
-
- mobio_files = []
-
- if protocol == 'grandtest-mobio':
-
- replay_files = self.replay_db.objects(
- protocol='grandtest', groups=groups, cls=purposes, **kwargs)
-
- replaymobile_files = self.replaymobile_db.objects(
- protocol='grandtest', groups=groups, cls=purposes, **kwargs)
-
- msu_mfsd_files = self.msu_mfsd_db.objects(
- group=groups, cls=purposes, **kwargs)
-
- mobio_files = self.get_mobio_files_given_single_group(
- groups=groups, purposes=purposes)
-
- if protocol is not None:
-
- if 'grandtest-train-eval' in protocol:
-
- if groups == 'train':
-
- replay_files = self.replay_db.objects(
- protocol='grandtest',
- groups=['train', 'devel'],
- cls=purposes,
- **kwargs)
-
- replaymobile_files = self.replaymobile_db.objects(
- protocol='grandtest',
- groups=['train', 'devel'],
- cls=purposes,
- **kwargs)
-
- msu_mfsd_files = self.msu_mfsd_db.objects(
- group=['train', 'devel'], cls=purposes, **kwargs)
-
- if len(protocol) > len('grandtest-train-eval'):
-
- num_train_samples = [
- int(s) for s in protocol.split("-") if s.isdigit()][-1]
-
- replay_files = self.uniform_select_list_elements(
- data=replay_files, n_samples=num_train_samples)
- replaymobile_files = self.uniform_select_list_elements(
- data=replaymobile_files, n_samples=num_train_samples)
- msu_mfsd_files = self.uniform_select_list_elements(
- data=msu_mfsd_files, n_samples=num_train_samples)
-
- if groups in ['devel', 'test']:
-
- replay_files = self.replay_db.objects(
- protocol='grandtest',
- groups='test',
- cls=purposes,
- **kwargs)
-
- replaymobile_files = self.replaymobile_db.objects(
- protocol='grandtest',
- groups='test',
- cls=purposes,
- **kwargs)
-
- msu_mfsd_files = self.msu_mfsd_db.objects(
- group='test', cls=purposes, **kwargs)
-
- return replay_files, replaymobile_files, msu_mfsd_files, mobio_files
-
- # =========================================================================
- def get_files_given_groups(self,
- groups=None,
- protocol=None,
- purposes=None,
- model_ids=None,
- **kwargs):
- """
- This function returns 4 lists of files for Raplay-Attack, Replay-Mobile,
- MSU MFSD and MOBIO databases, which fulfill the given restrictions. This
- function for the groups parameter accepts a single string OR a list
- of strings with multiple groups. Group names are low level, see
- ``low_level_group_names`` argument of the class for available options.
-
- Keyword parameters:
-
- ``groups`` : :py:class:`str`
- OR a list of strings.
- The groups of which the clients should be returned.
- Usually, groups are one or more elements of ('train', 'devel', 'test').
-
- ``protocol`` : :py:class:`str`
- The protocol for which the clients should be retrieved.
- Available options are defined in the ``available_protocols`` argument
- of the class. So far the following protocols are available:
-
- 1. "grandtest" - this protocol is using all the data available in the
- databases Replay-Attack, Replay-Mobile, MSU MFSD.
-
- 2. "photo-photo-video" - this protocol is used to test the system on
- unseen types of attacks. In this case the attacks are splitted
- as follows:
- 'train' set - only **photo** attacks are used for training,
- 'dev' set - only **photo** attacks are used for threshold tuning,
- 'eval' set - only **video** attacks are used in final evaluation.
- In this case the final performance is estimated on previously
- unseen **video** attacks.
-
- 3. "video-video-photo" - this protocol is used to test the system on
- unseen types of attacks. In this case the attacks are splitted
- as follows:
- 'train' set - only **video** attacks are used for training,
- 'dev' set - only **video** attacks are used for threshold tuning,
- 'eval' set - only **photo** attacks are used in final evaluation.
- In this case the final performance is estimated on previously
- unseen **photo** attacks.
-
- 4. "grandtest-mobio" - this protocol is using all the data available in the
- databases Replay-Attack, Replay-Mobile, MSU MFSD plus some additional data
- from MOBIO dataset is used in the training set.
-
- 5. "grandtest-train-eval" - this protocol is using all the data available
- in the databases Replay-Attack, Replay-Mobile, MSU MFSD. Only two gropus
- 'train' and 'test' are available in this protocol. The 'devel' set is
- concatenated to the training data. When requesting 'devel' set, the
- data of the 'test' set is returned.
-
- 6. "grandtest-train-eval-<num_train_samples>" -
- this protocol is using all the data available in the databases
- Replay-Attack, Replay-Mobile, MSU MFSD. Only two gropus
- 'train' and 'eval' are available in this protocol. The 'dev' set is
- concatenated to the training data. When requesting 'dev' set, the
- data of the 'eval' set is returned.
- MOREOVER, in this protocol you can specify the number of training samples
- <num_train_samples>, which will be uniformly selected for each database
- (Replay-Attack, Replay-Mobile, MSU MFSD) used in the Aggregated DB.
- For example, in the protocol "grandtest-train-eval-5", 5 training samples
- will be selected for Replay-Attack, 5 for Replay-Mobile, and 5 for
- MSU MFSD. The total number of training samples is 15 in this case.
-
- ``purposes`` : :py:class:`str`
- OR a list of strings.
- The purposes for which File objects should be retrieved.
- Usually it is either 'real' or 'attack'.
-
- ``model_ids``
- This parameter is not supported in PAD databases yet
-
- **Returns:**
-
- ``replay_files`` : [File]
- A list of files corresponding to Replay-Attack database.
-
- ``replaymobile_files`` : [File]
- A list of files corresponding to Replay-Mobile database.
-
- ``msu_mfsd_files`` : [File]
- A list of files corresponding to MSU MFSD database.
-
- ``mobio_files`` : [File]
- A list of files corresponding to MOBIO database or an empty list.
- """
-
- if isinstance(groups,
- str) or groups is None: # if a single group is given
-
- groups = [groups]
-
- replay_files = []
-
- replaymobile_files = []
-
- msu_mfsd_files = []
-
- mobio_files = []
-
- for group in groups:
-
- files = self.get_files_given_single_group(
- groups=group,
- protocol=protocol,
- purposes=purposes,
- model_ids=model_ids,
- **kwargs)
-
- replay_files += files[0]
-
- replaymobile_files += files[1]
-
- msu_mfsd_files += files[2]
-
- mobio_files += files[3]
-
- return replay_files, replaymobile_files, msu_mfsd_files, mobio_files
-
- # =========================================================================
- def objects(self,
- groups=None,
- protocol=None,
- purposes=None,
- model_ids=None,
- **kwargs):
- """
- This function returns a list of AggregatedDbPadFile objects, which fulfill the given restrictions.
-
- Keyword parameters:
-
- ``groups`` : :py:class:`str`
- OR a list of strings.
- The groups of which the clients should be returned.
- Usually, groups are one or more elements of ('train', 'dev', 'eval')
-
- ``protocol`` : :py:class:`str`
- The protocol for which the clients should be retrieved.
- Available options are defined in the ``available_protocols`` argument
- of the class. So far the following protocols are available:
-
- 1. "grandtest" - this protocol is using all the data available in the
- databases Replay-Attack, Replay-Mobile, MSU MFSD.
-
- 2. "photo-photo-video" - this protocol is used to test the system on
- unseen types of attacks. In this case the attacks are splitted
- as follows:
- 'train' set - only **photo** attacks are used for training,
- 'dev' set - only **photo** attacks are used for threshold tuning,
- 'eval' set - only **video** attacks are used in final evaluation.
- In this case the final performance is estimated on previously
- unseen **video** attacks.
-
- 3. "video-video-photo" - this protocol is used to test the system on
- unseen types of attacks. In this case the attacks are splitted
- as follows:
- 'train' set - only **video** attacks are used for training,
- 'dev' set - only **video** attacks are used for threshold tuning,
- 'eval' set - only **photo** attacks are used in final evaluation.
- In this case the final performance is estimated on previously
- unseen **photo** attacks.
-
- 4. "grandtest-mobio" - this protocol is using all the data available in the
- databases Replay-Attack, Replay-Mobile, MSU MFSD plus some additional data
- from MOBIO dataset is used in the training set.
-
- 5. "grandtest-train-eval" - this protocol is using all the data available
- in the databases Replay-Attack, Replay-Mobile, MSU MFSD. Only two gropus
- 'train' and 'eval' are available in this protocol. The 'dev' set is
- concatenated to the training data. When requesting 'dev' set, the
- data of the 'eval' set is returned.
-
- 6. "grandtest-train-eval-<num_train_samples>" -
- this protocol is using all the data available in the databases
- Replay-Attack, Replay-Mobile, MSU MFSD. Only two gropus
- 'train' and 'eval' are available in this protocol. The 'dev' set is
- concatenated to the training data. When requesting 'dev' set, the
- data of the 'eval' set is returned.
- MOREOVER, in this protocol you can specify the number of training samples
- <num_train_samples>, which will be uniformly selected for each database
- (Replay-Attack, Replay-Mobile, MSU MFSD) used in the Aggregated DB.
- For example, in the protocol "grandtest-train-eval-5", 5 training samples
- will be selected for Replay-Attack, 5 for Replay-Mobile, and 5 for
- MSU MFSD. The total number of training samples is 15 in this case.
-
- ``purposes`` : :py:class:`str`
- OR a list of strings.
- The purposes for which File objects should be retrieved.
- Usually it is either 'real' or 'attack'.
-
- ``model_ids``
- This parameter is not supported in PAD databases yet
-
- **Returns:**
-
- ``files`` : [AggregatedDbPadFile]
- A list of AggregatedDbPadFile objects.
- """
-
- # Convert group names to low-level group names here.
- groups = self.convert_names_to_lowlevel(
- groups, self.low_level_group_names, self.high_level_group_names)
- # Since this database was designed for PAD experiments, nothing special
- # needs to be done here.
-
- replay_files, replaymobile_files, msu_mfsd_files, mobio_files = self.get_files_given_groups(
- groups=groups,
- protocol=protocol,
- purposes=purposes,
- model_ids=model_ids,
- **kwargs)
-
- files = replay_files + replaymobile_files + msu_mfsd_files + \
- mobio_files # append all files to a single list
-
- files = [AggregatedDbPadFile(f) for f in files]
-
- return files
-
- # =========================================================================
- def annotations(self, f):
- """
- Return annotations for a given file object ``f``, which is an instance
- of ``AggregatedDbPadFile`` defined in the HLDI of the Aggregated DB.
- The ``load()`` method of ``AggregatedDbPadFile`` class (see above)
- returns a video, therefore this method returns bounding-box annotations
- for each video frame. The annotations are returned as dictionary of
- dictionaries.
-
- **Parameters:**
-
- ``f`` : :py:class:`object`
- An instance of ``AggregatedDbPadFile`` defined above.
-
- **Returns:**
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing the annotations for each frame in the video.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col)}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
-
- """
-
- import bob.db.replay
- import bob.db.replaymobile
- import bob.db.msu_mfsd_mod
-
- directories = self.original_directory.split(" ")
-
- if isinstance(
- f.f, bob.db.replay.models.File
- ): # check if instance of File class of LLDI of Replay-Attack
-
- hldi_db = replay_hldi.ReplayPadDatabase(
- original_directory=directories[0])
-
- if isinstance(
- f.f, bob.db.replaymobile.models.File
- ): # check if instance of File class of LLDI of Replay-Mobile
-
- hldi_db = replay_mobile_hldi.ReplayMobilePadDatabase(
- original_directory=directories[1])
-
- if isinstance(f.f, bob.db.msu_mfsd_mod.models.File
- ): # check if instance of File class of LLDI of MSU MFSD
-
- hldi_db = msu_mfsd_hldi.MsuMfsdPadDatabase(
- original_directory=directories[2])
-
- if self.protocol == "grandtest-mobio" or isinstance(
- f.f, bob.db.mobio.models.File
- ): # annotations are not available for this protocol
-
- annotations = {}
-
- else:
-
- annotations = hldi_db.annotations(f)
-
- return annotations
diff --git a/bob/pad/face/database/msu_mfsd.py b/bob/pad/face/database/msu_mfsd.py
deleted file mode 100644
index 59e055a8dad5d6c00e4f43df64cee32ae6856580..0000000000000000000000000000000000000000
--- a/bob/pad/face/database/msu_mfsd.py
+++ /dev/null
@@ -1,236 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-
-from bob.bio.video import FrameSelector, FrameContainer
-from bob.pad.face.database import VideoPadFile # Used in MsuMfsdPadFile class
-from bob.pad.base.database import PadDatabase
-from bob.extension import rc
-import os
-import numpy as np
-
-
-class MsuMfsdPadFile(VideoPadFile):
- """
- A high level implementation of the File class for the MSU MFSD database.
- """
-
- def __init__(self, f):
- """
- **Parameters:**
-
- ``f`` : :py:class:`object`
- An instance of the File class defined in the low level db interface
- of the MSU MFSD database, in the bob.db.msu_mfsd_mod.models.py file.
- """
-
- self.f = f
- # this f is actually an instance of the File class that is defined in
- # bob.db.msu_mfsd_mod.models and the PadFile class here needs
- # client_id, path, attack_type, file_id for initialization. We have to
- # convert information here and provide them to PadFile. attack_type is a
- # little tricky to get here. Based on the documentation of PadFile:
- # In cased of a spoofed data, this parameter should indicate what kind of spoofed attack it is.
- # The default None value is interpreted that the PadFile is a genuine or real sample.
- if f.is_real():
- attack_type = None
- else:
- attack_type = "attack"
- # attack_type is a string and I decided to make it like this for this
- # particular database. You can do whatever you want for your own database.
-
- super(MsuMfsdPadFile, self).__init__(
- client_id=f.client_id, path=f.path, attack_type=attack_type, file_id=f.id
- )
-
- def load(
- self,
- directory=None,
- extension=None,
- frame_selector=FrameSelector(selection_style="all"),
- ):
- """
- Overridden version of the load method defined in the ``VideoPadFile``.
-
- **Parameters:**
-
- ``directory`` : :py:class:`str`
- String containing the path to the MSU MFSD database.
- Default: None
-
- ``extension`` : :py:class:`str`
- Extension of the video files in the MSU MFSD database.
- Note: ``extension`` value is not used in the code of this method.
- Default: None
-
- ``frame_selector`` : ``FrameSelector``
- The frame selector to use.
-
- **Returns:**
-
- ``video_data`` : FrameContainer
- Video data stored in the FrameContainer, see ``bob.bio.video.utils.FrameContainer``
- for further details.
- """
-
- _, extension = os.path.splitext(self.f.videofile()) # get file extension
-
- video_data_array = self.f.load(directory=directory, extension=extension)
- return frame_selector(video_data_array)
-
-
-class MsuMfsdPadDatabase(PadDatabase):
- """
- A high level implementation of the Database class for the MSU MFSD database.
- """
-
- def __init__(
- self,
- protocol="grandtest", # grandtest is the default protocol for this database
- original_directory=None,
- original_extension=None,
- annotation_directory=None,
- annotation_extension='.json',
- annotation_type='json',
- **kwargs
- ):
- """
- **Parameters:**
-
- ``protocol`` : :py:class:`str` or ``None``
- The name of the protocol that defines the default experimental setup for this database.
-
- ``original_directory`` : :py:class:`str`
- The directory where the original data of the database are stored.
-
- ``original_extension`` : :py:class:`str`
- The file name extension of the original data.
-
- ``kwargs``
- The arguments of the :py:class:`bob.bio.base.database.BioDatabase` base class constructor.
- """
-
- from bob.db.msu_mfsd_mod import Database as LowLevelDatabase
-
- self.db = LowLevelDatabase()
-
- # Since the high level API expects different group names than what the low
- # level API offers, you need to convert them when necessary
- self.low_level_group_names = (
- "train",
- "devel",
- "test",
- ) # group names in the low-level database interface
- self.high_level_group_names = (
- "train",
- "dev",
- "eval",
- ) # names are expected to be like that in objects() function
-
- # Always use super to call parent class methods.
- super(MsuMfsdPadDatabase, self).__init__(
- name="msu-mfsd",
- protocol=protocol,
- original_directory=original_directory,
- original_extension=original_extension,
- **kwargs
- )
-
- @property
- def original_directory(self):
- return self.db.original_directory
-
- @original_directory.setter
- def original_directory(self, value):
- self.db.original_directory = value
-
- def objects(
- self, groups=None, protocol=None, purposes=None, model_ids=None, **kwargs
- ):
- """
- This function returns lists of MsuMfsdPadFile objects, which fulfill the given restrictions.
-
- Keyword parameters:
-
- ``groups`` : :py:class:`str`
- OR a list of strings.
- The groups of which the clients should be returned.
- Usually, groups are one or more elements of ('train', 'dev', 'eval')
-
- ``protocol`` : :py:class:`str`
- The protocol for which the clients should be retrieved.
- Note: this argument is not used in the code, because ``objects`` method of the
- low-level BD interface of the MSU MFSD doesn't have ``protocol`` argument.
-
- ``purposes`` : :py:class:`str`
- OR a list of strings.
- The purposes for which File objects should be retrieved.
- Usually it is either 'real' or 'attack'.
-
- ``model_ids``
- This parameter is not supported in PAD databases yet.
-
- **Returns:**
-
- ``files`` : [MsuMfsdPadFile]
- A list of MsuMfsdPadFile objects.
- """
-
- # Convert group names to low-level group names here.
- groups = self.convert_names_to_lowlevel(
- groups, self.low_level_group_names, self.high_level_group_names
- )
- # Since this database was designed for PAD experiments, nothing special
- # needs to be done here.
- files = self.db.objects(group=groups, cls=purposes, **kwargs)
-
- files = [MsuMfsdPadFile(f) for f in files]
- for f in files:
- f.original_directory = self.original_directory
- f.annotation_directory = self.annotation_directory
- f.annotation_extension = self.annotation_extension
- f.annotation_type = self.annotation_type
-
- return files
-
- def annotations(self, f):
- """
- Return annotations for a given file object ``f``, which is an instance
- of ``MsuMfsdPadFile`` defined in the HLDI of the MSU MFSD DB.
- The ``load()`` method of ``MsuMfsdPadFile`` class (see above)
- returns a video, therefore this method returns bounding-box annotations
- for each video frame. The annotations are returned as dictionary of dictionaries.
-
- **Parameters:**
-
- ``f`` : :py:class:`object`
- An instance of ``MsuMfsdPadFile`` defined above.
-
- **Returns:**
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing the annotations for each frame in the video.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col)}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
- """
-
- annots = f.f.bbx(
- directory=self.original_directory
- ) # numpy array containing the face bounding box data for each video frame, returned data format described in the f.bbx() method of the low level interface
-
- annotations = {} # dictionary to return
-
- for frame_annots in annots:
-
- topleft = (np.int(frame_annots[2]), np.int(frame_annots[1]))
- bottomright = (
- np.int(frame_annots[2] + frame_annots[4]),
- np.int(frame_annots[1] + frame_annots[3]),
- )
-
- annotations[str(np.int(frame_annots[0]))] = {
- "topleft": topleft,
- "bottomright": bottomright,
- }
-
- return annotations
diff --git a/bob/pad/face/test/test_databases.py b/bob/pad/face/test/test_databases.py
index 0dd94cdc2cca4f5b77c80a44d1d3702430c16c51..66852f13f339ca681e4e7f5e7db6b6a0516f91aa 100644
--- a/bob/pad/face/test/test_databases.py
+++ b/bob/pad/face/test/test_databases.py
@@ -77,38 +77,6 @@ def test_replaymobile():
% e)
-@db_available('msu_mfsd_mod')
-def test_msu_mfsd():
- msu_mfsd = bob.bio.base.load_resource(
- 'msu-mfsd',
- 'database',
- preferred_package='bob.pad.face',
- package_prefix='bob.pad.')
- try:
-
- assert len(msu_mfsd.objects(groups=['train', 'dev', 'eval'])) == 280
- assert len(msu_mfsd.objects(groups=['train', 'dev'])) == 160
- assert len(msu_mfsd.objects(groups=['train'])) == 80
- assert len(
- msu_mfsd.objects(
- groups=['train', 'dev', 'eval'], protocol='grandtest')) == 280
- assert len(
- msu_mfsd.objects(
- groups=['train', 'dev', 'eval'],
- protocol='grandtest',
- purposes='real')) == 70
- assert len(
- msu_mfsd.objects(
- groups=['train', 'dev', 'eval'],
- protocol='grandtest',
- purposes='attack')) == 210
-
- except IOError as e:
- raise SkipTest(
- "The database could not be queried; probably the db.sql3 file is missing. Here is the error: '%s'"
- % e)
-
-
# Test the maskattack database
@db_available('maskattack')
def test_maskattack():
@@ -145,70 +113,6 @@ def test_maskattack():
"The database could not be queried; probably the db.sql3 file is missing. Here is the error: '%s'"
% e)
-# Test the Aggregated database, which doesn't have a package
-
-@db_available('replay')
-@db_available('replaymobile')
-@db_available('msu_mfsd_mod')
-@db_available('mobio')
-def test_aggregated_db():
- aggregated_db = bob.bio.base.load_resource(
- 'aggregated-db',
- 'database',
- preferred_package='bob.pad.face',
- package_prefix='bob.pad.')
- try:
-
- assert len(
- aggregated_db.objects(groups=['train', 'dev', 'eval'])) == 2510
- assert len(aggregated_db.objects(groups=['train', 'dev'])) == 1608
- assert len(aggregated_db.objects(groups=['train'])) == 752
-
- assert len(aggregated_db.objects(groups='train')) == 752
- assert len(aggregated_db.objects(groups='dev')) == 856
- assert len(aggregated_db.objects(groups='eval')) == 902
-
- assert len(
- aggregated_db.objects(
- groups=['train', 'dev', 'eval'], protocol='grandtest')) == 2510
- assert len(
- aggregated_db.objects(
- groups=['train', 'dev', 'eval'],
- protocol='grandtest',
- purposes='real')) == 660
- assert len(
- aggregated_db.objects(
- groups=['train', 'dev', 'eval'],
- protocol='grandtest',
- purposes='attack')) == 1850
-
- assert len(
- aggregated_db.objects(
- groups=['train', 'dev', 'eval'],
- protocol='photo-photo-video')) == 1664
- assert len(
- aggregated_db.objects(
- groups=['train', 'dev'], protocol='photo-photo-video')) == 1176
- assert len(
- aggregated_db.objects(groups='eval',
- protocol='photo-photo-video')) == 488
-
- assert len(
- aggregated_db.objects(
- groups=['train', 'dev', 'eval'],
- protocol='video-video-photo')) == 1506
- assert len(
- aggregated_db.objects(
- groups=['train', 'dev'], protocol='video-video-photo')) == 872
- assert len(
- aggregated_db.objects(groups='eval',
- protocol='video-video-photo')) == 634
-
- except IOError as e:
- raise SkipTest(
- "The database could not be queried; probably the db.sql3 file is missing. Here is the error: '%s'"
- % e)
-
# Test the casiasurf database
@db_available('casiasurf')
diff --git a/conda/meta.yaml b/conda/meta.yaml
index c1ef93124c441501c20ebb2f9bdf6d60b144ebf8..7e692c2ee6b213e1034685ab9215fd997b61bb40 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -66,7 +66,6 @@ test:
- gridtk
- bob.db.replay
- bob.db.replaymobile
- - bob.db.msu_mfsd_mod
- bob.db.casia_fasd
- bob.db.mobio
- bob.db.maskattack
diff --git a/doc/api.rst b/doc/api.rst
index 04e403c56f78a833559fa1b07102fc28f03e0f65..cdd7949294cd90cf8b2b8deb05b2671a1e179473 100644
--- a/doc/api.rst
+++ b/doc/api.rst
@@ -30,18 +30,6 @@ REPLAY-MOBILE Database
.. autoclass:: bob.pad.face.database.replay_mobile.ReplayMobilePadFile
.. autoclass:: bob.pad.face.database.replay_mobile.ReplayMobilePadDatabase
-MSU MFSD Database
-========================
-
-.. autoclass:: bob.pad.face.database.msu_mfsd.MsuMfsdPadFile
-.. autoclass:: bob.pad.face.database.msu_mfsd.MsuMfsdPadDatabase
-
-Aggregated Database
-========================
-
-.. autoclass:: bob.pad.face.database.aggregated_db.AggregatedDbPadFile
-.. autoclass:: bob.pad.face.database.aggregated_db.AggregatedDbPadDatabase
-
MIFS Database
========================
diff --git a/doc/baselines.rst b/doc/baselines.rst
index fb08dc5201e358b426a5752066cf1b69891ca79c..bbe86cf5fb1b35ae4a60a76aa1cccf8baca12559 100644
--- a/doc/baselines.rst
+++ b/doc/baselines.rst
@@ -388,310 +388,6 @@ The ROC curves for the particular experiment can be downloaded from here:
------------
-.. _bob.pad.face.baselines.msu_mfsd:
-
-Baselines on MSU MFSD database
---------------------------------------
-
-This section summarizes the results of baseline face PAD experiments on the `MSU MFSD`_ database.
-The description of the database-related settings, which are used to run face PAD baselines on the MSU MFSD is given here :ref:`bob.pad.face.resources.databases.msu_mfsd`. To understand the settings in more details you can check the corresponding configuration file : ``bob/pad/face/config/msu_mfsd.py``.
-
-
-LBP features of facial region + SVM classifier
-========================================================================
-
-Detailed description of this PAD pipe-line is given at :ref:`bob.pad.face.resources.face_pad.lbp_svm_replayattack`.
-Note, that the same PAD pipe-line was used to run experiments on the Replay-Attack database.
-
-To run this baseline on the `MSU MFSD`_ database, using the ``grandtest`` protocol, execute the following:
-
-.. code-block:: sh
-
- $ spoof.py msu-mfsd lbp-svm \
- --sub-directory <PATH_TO_STORE_THE_RESULTS>
-
-.. tip::
-
- Similarly to the tip above you can run this baseline in parallel.
-
-To understand the settings of this baseline PAD experiment you can check the
-corresponding configuration file: ``bob/pad/face/config/lbp_svm.py``
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-dev \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-eval \
- --legends "LBP features of facial region + SVM classifier + MSU MFSD database" \
- -e \
- --criterion eer \
- -o <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
-
-The EER/HTER errors for the `MSU MFSD`_ database are summarized in the Table below:
-
-+-------------------+----------+----------+
-| Protocol | EER,\% | HTER,\% |
-+===================+==========+==========+
-| ``grandtest`` | 27.402 | 21.399 |
-+-------------------+----------+----------+
-
-The ROC curves for the particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_lbp_svm_msu_mfsd.pdf>`
-
-------------
-
-
-Image Quality Measures as features of facial region + SVM classifier
-========================================================================
-
-Detailed description of this PAD pipe-line is given at :ref:`bob.pad.face.resources.face_pad.qm_svm_replayattack`.
-Note, that the same PAD pipe-line was used to run experiments on the Replay-Attack database.
-
-To run this baseline on the `MSU MFSD`_ database, using the ``grandtest`` protocol, execute the following:
-
-.. code-block:: sh
-
- $ spoof.py msu-mfsd qm-svm \
- --sub-directory <PATH_TO_STORE_THE_RESULTS>
-
-.. tip::
-
- Similarly to the tip above you can run this baseline in parallel.
-
-To understand the settings of this baseline PAD experiment you can check the
-corresponding configuration file: ``bob/pad/face/config/qm_svm.py``
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-dev \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-eval \
- --legends "IQM features of facial region + SVM classifier + MSU MFSD database" \
- -e \
- --criterion eer \
- -o <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
-
-The EER/HTER errors for the `MSU MFSD`_ database are summarized in the Table below:
-
-+-------------------+----------+----------+
-| Protocol | EER,\% | HTER,\% |
-+===================+==========+==========+
-| ``grandtest`` | 3.665 | 4.944 |
-+-------------------+----------+----------+
-
-The ROC curves for the particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_iqm_svm_msu_mfsd.pdf>`
-
-------------
-
-
-Frame differences based features (motion analysis) + SVM classifier
-========================================================================
-
-Detailed description of this PAD pipe-line is given at :ref:`bob.pad.face.resources.face_pad.frame_diff_svm_replayattack`.
-Note, that the same PAD pipe-line was used to run experiments on the Replay-Attack database.
-
-To run this baseline on the `MSU MFSD`_ database, using the ``grandtest`` protocol, execute the following:
-
-.. code-block:: sh
-
- $ spoof.py msu-mfsd frame-diff-svm \
- --sub-directory <PATH_TO_STORE_THE_RESULTS>
-
-.. tip::
-
- Similarly to the tip above you can run this baseline in parallel.
-
-To understand the settings of this baseline PAD experiment you can check the
-corresponding configuration file: ``bob/pad/face/config/frame_diff_svm.py``
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-dev \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-eval \
- --legends "10 features for each window in Frame Differences + SVM classifier + MSU MFSD database" \
- -e \
- --criterion eer \
- -o <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
-
-The EER/HTER errors for the `MSU MFSD`_ database are summarized in the Table below:
-
-+-------------------+----------+----------+
-| Protocol | EER,\% | HTER,\% |
-+===================+==========+==========+
-| ``grandtest`` | 25.839 | 17.050 |
-+-------------------+----------+----------+
-
-The ROC curves for the particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_frame_diff_svm_msu_mfsd.pdf>`
-
-------------
-
-
-.. _bob.pad.face.baselines.aggregated_db:
-
-Baselines on Aggregated Database
---------------------------------------
-
-This section summarizes the results of baseline face PAD experiments on the Aggregated Database.
-The description of the database-related settings, which are used to run face PAD baselines on the Aggregated Db is given here :ref:`bob.pad.face.resources.databases.aggregated_db`. To understand the settings in more details you can check the corresponding configuration file : ``bob/pad/face/config/aggregated_db.py``.
-
-------------
-
-
-LBP features of facial region + SVM classifier
-========================================================================
-
-Detailed description of this PAD pipe-line is given at :ref:`bob.pad.face.resources.face_pad.lbp_svm_aggregated_db`.
-
-To run this baseline on the :ref:`bob.pad.face.resources.databases.aggregated_db` database, using the ``grandtest`` protocol, execute the following:
-
-.. code-block:: sh
-
- $ spoof.py aggregated-db lbp-svm-aggregated-db \
- --sub-directory <PATH_TO_STORE_THE_RESULTS>
-
-.. tip::
-
- Similarly to the tip above you can run this baseline in parallel.
-
-To understand the settings of this baseline PAD experiment you can check the
-corresponding configuration file: ``bob/pad/face/config/lbp_svm_aggregated_db.py``
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-dev \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-eval \
- --legends "LBP features of facial region + SVM classifier + Aggregated database" \
- -e \
- --criterion eer \
- -o <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
-
-The EER/HTER errors for the :ref:`bob.pad.face.resources.databases.aggregated_db` database are summarized in the Table below:
-
-+-------------------+----------+----------+
-| Protocol | EER,\% | HTER,\% |
-+===================+==========+==========+
-| ``grandtest`` | 17.490 | 19.705 |
-+-------------------+----------+----------+
-
-The ROC curves for the particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_lbp_svm_aggregated_db.pdf>`
-
-------------
-
-
-Image Quality Measures as features of facial region + SVM classifier
-========================================================================
-
-Detailed description of this PAD pipe-line is given at :ref:`bob.pad.face.resources.face_pad.qm_svm_aggregated_db`.
-
-To run this baseline on the :ref:`bob.pad.face.resources.databases.aggregated_db` database, using the ``grandtest`` protocol, execute the following:
-
-.. code-block:: sh
-
- $ spoof.py aggregated-db qm-svm-aggregated-db \
- --sub-directory <PATH_TO_STORE_THE_RESULTS>
-
-.. tip::
-
- Similarly to the tip above you can run this baseline in parallel.
-
-To understand the settings of this baseline PAD experiment you can check the
-corresponding configuration file: ``bob/pad/face/config/qm_svm_aggregated_db.py``
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-dev \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-eval \
- --legends "IQM features of facial region + SVM classifier + Aggregated database" \
- -e \
- --criterion eer \
- -o <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
-
-The EER/HTER errors for the :ref:`bob.pad.face.resources.databases.aggregated_db` database are summarized in the Table below:
-
-+-------------------+----------+----------+
-| Protocol | EER,\% | HTER,\% |
-+===================+==========+==========+
-| ``grandtest`` | 12.710 | 15.253 |
-+-------------------+----------+----------+
-
-The ROC curves for the particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_qm_svm_aggregated_db.pdf>`
-
-------------
-
-
-Frame differences based features (motion analysis) + SVM classifier
-========================================================================
-
-Detailed description of this PAD pipe-line is given at :ref:`bob.pad.face.resources.face_pad.frame_diff_svm_aggregated_db`.
-
-To run this baseline on the :ref:`bob.pad.face.resources.databases.aggregated_db` database, using the ``grandtest`` protocol, execute the following:
-
-.. code-block:: sh
-
- $ spoof.py aggregated-db frame-diff-svm-aggregated-db \
- --sub-directory <PATH_TO_STORE_THE_RESULTS>
-
-.. tip::
-
- Similarly to the tip above you can run this baseline in parallel.
-
-To understand the settings of this baseline PAD experiment you can check the
-corresponding configuration file: ``bob/pad/face/config/frame_diff_svm_aggregated_db.py``
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-dev \
- <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-eval \
- --legends "10 features for each window in Frame Differences + SVM classifier + Aggregated Db" \
- -e \
- --criterion eer \
- -o <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
-
-The EER/HTER errors for the :ref:`bob.pad.face.resources.databases.aggregated_db` database are summarized in the Table below:
-
-+-------------------+----------+----------+
-| Protocol | EER,\% | HTER,\% |
-+===================+==========+==========+
-| ``grandtest`` | 35.219 | 43.029 |
-+-------------------+----------+----------+
-
-The ROC curves for the particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_frame_diff_svm_aggregated_db.pdf>`
-
-------------
-
.. _bob.pad.face.baselines.other_db:
diff --git a/doc/installation.rst b/doc/installation.rst
index 19849aa9263e622d0e0afca1757c57f360f6843e..d1a91ddfa8922cfb275f6e7db2949fcb0a7cb121 100644
--- a/doc/installation.rst
+++ b/doc/installation.rst
@@ -25,8 +25,6 @@ The current system readily supports the following freely available datasets:
* `REPLAYATTACK`_
* `REPLAY-MOBILE`_
-* `MSU MFSD`_
-* ``Aggregated DB``
After downloading the databases, annotate the base directories in which they
are installed. Then, follow the instructions in
diff --git a/doc/links.rst b/doc/links.rst
index edcab972d9aac9b197a193ae8a48b72009d601c0..cd652a07f1baf7c55dcfffae9ba14ee847d2c817 100644
--- a/doc/links.rst
+++ b/doc/links.rst
@@ -12,6 +12,5 @@
.. _replayattack: https://www.idiap.ch/dataset/replayattack
.. _replay-mobile: https://www.idiap.ch/dataset/replay-mobile
.. _dependencies: https://gitlab.idiap.ch/bob/bob/wikis/Dependencies
-.. _MSU MFSD: http://biometrics.cse.msu.edu/Publications/Databases/MSUMobileFaceSpoofing/index.htm
.. _MIFS: http://www.antitza.com/makeup-datasets.html
.. _CELEBA: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
diff --git a/doc/other_pad_algorithms.rst b/doc/other_pad_algorithms.rst
index 9a515284eaec53d497460430ee7f06cf39f001cd..39e6b918854a2dde993d878a7dbd564fffe797d6 100644
--- a/doc/other_pad_algorithms.rst
+++ b/doc/other_pad_algorithms.rst
@@ -84,256 +84,6 @@ is available on the section :ref:`bob.pad.face.resources`.
Once this step is done, you can proceed with the instructions below.
-------------
-
-
-.. _bob.pad.face.other_pad_algorithms.aggregated_db:
-
-Anomaly detection based PAD on Aggregated Database
---------------------------------------------------------
-
-This section summarizes the results of *anomaly detection* based face PAD experiments on the Aggregated Database.
-The description of the database-related settings, which are used to run face PAD algorithms on the Aggregated Db is given here :ref:`bob.pad.face.resources.databases.aggregated_db`. To understand the settings in more details you can check the corresponding configuration file : ``bob/pad/face/config/aggregated_db.py``.
-
-------------
-
-
-Results for *grandtest* protocol
-========================================================================
-
-This section summarizes the evaluation results on the **grandtest** protocol of the Aggregated database for the following face PAD algorithms (for more details click on the corresponding algorithm):
-
-- :ref:`bob.pad.face.resources.face_pad.qm_one_class_gmm`,
-- :ref:`bob.pad.face.resources.face_pad.qm_one_class_svm_aggregated_db`,
-- :ref:`bob.pad.face.resources.face_pad.qm_lr`,
-- :ref:`bob.pad.face.resources.face_pad.qm_svm_aggregated_db`.
-
-For a more detailed understanding of above pipe-lines you can also check corresponding configuration files:
-
-- ``bob/pad/face/config/qm_one_class_gmm.py``,
-- ``bob/pad/face/config/qm_one_class_svm_aggregated_db.py``,
-- ``bob/pad/face/config/qm_lr.py``,
-- ``bob/pad/face/config/qm_svm_aggregated_db.py``.
-
-To run above algorithms on the :ref:`bob.pad.face.resources.databases.aggregated_db` database, using the ``grandtest`` protocol, execute the following:
-
-.. code-block:: sh
-
- $ spoof.py aggregated-db qm-one-class-gmm \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_1>
-
- $ spoof.py aggregated-db qm-one-class-svm-aggregated-db \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_2>
-
- $ spoof.py aggregated-db qm-lr \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_3>
-
- $ spoof.py aggregated-db qm-svm-aggregated-db \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_4>
-
-.. tip::
-
- If you are in `idiap`_ you can use SGE grid to speed-up the calculations.
- Simply add ``--grid idiap`` argument to the above command. For example:
-
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>_{1,2,3,4}/grandtest/scores/scores-{dev,eval} \
- --legends \
- "IQM + one-class GMM + Aggregated Db" \
- "IQM + one-class SVM + Aggregated Db" \
- "IQM + two-class LR + Aggregated Db" \
- "IQM + two-class SVM + Aggregated Db" \
- -e \
- --c eer \
- -o <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
-
-The EER/HTER errors for the :ref:`bob.pad.face.resources.databases.aggregated_db` database are summarized in the Table below:
-
-+------------------------+----------+----------+
-| Algorithm | EER,\% | HTER,\% |
-+========================+==========+==========+
-| IQM + one-class GMM | 19.336 | 20.769 |
-+------------------------+----------+----------+
-| IQM + one-class SVM | 28.137 | 34.776 |
-+------------------------+----------+----------+
-| IQM + two-class LR | 10.354 | 11.856 |
-+------------------------+----------+----------+
-| IQM + two-class SVM | 12.710 | 15.253 |
-+------------------------+----------+----------+
-
-The ROC curves for the particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_iqm_anomaly_detection_aggr_db_grandtest.pdf>`
-
-------------
-
-
-Results for *photo-photo-video* protocol
-========================================================================
-
-This section summarizes the evaluation results on the **photo-photo-video** protocol of the Aggregated database for the following face PAD algorithms (for more details click on the corresponding algorithm):
-
-- :ref:`bob.pad.face.resources.face_pad.qm_one_class_gmm`,
-- :ref:`bob.pad.face.resources.face_pad.qm_one_class_svm_aggregated_db`,
-- :ref:`bob.pad.face.resources.face_pad.qm_lr`,
-- :ref:`bob.pad.face.resources.face_pad.qm_svm_aggregated_db`.
-
-For a more detailed understanding of above pipe-lines you can also check corresponding configuration files:
-
-- ``bob/pad/face/config/qm_one_class_gmm.py``,
-- ``bob/pad/face/config/qm_one_class_svm_aggregated_db.py``,
-- ``bob/pad/face/config/qm_lr.py``,
-- ``bob/pad/face/config/qm_svm_aggregated_db.py``.
-
-To run above algorithms on the :ref:`bob.pad.face.resources.databases.aggregated_db` database, using the ``photo-photo-video`` protocol, execute the following:
-
-.. code-block:: sh
-
- $ spoof.py aggregated-db qm-one-class-gmm \
- --protocol photo-photo-video \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_1>
-
- $ spoof.py aggregated-db qm-one-class-svm-aggregated-db \
- --protocol photo-photo-video \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_2>
-
- $ spoof.py aggregated-db qm-lr \
- --protocol photo-photo-video \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_3>
-
- $ spoof.py aggregated-db qm-svm-aggregated-db \
- --protocol photo-photo-video \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_4>
-
-.. tip::
-
- If you are in `idiap`_ you can use SGE grid to speed-up the calculations.
- Simply add ``--grid idiap`` argument to the above command. For example:
-
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>_{1,2,3,4}/photo-photo-video/scores/scores-{dev,eval} \
- --legends \
- "IQM + one-class GMM + Aggregated Db" \
- "IQM + one-class SVM + Aggregated Db" \
- "IQM + two-class LR + Aggregated Db" \
- "IQM + two-class SVM + Aggregated Db" \
- -e \
- --criterion eer \
- -o <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
-
-The EER/HTER errors for the :ref:`bob.pad.face.resources.databases.aggregated_db` database are summarized in the Table below:
-
-+------------------------+----------+----------+
-| Algorithm | EER,\% | HTER,\% |
-+========================+==========+==========+
-| IQM + one-class GMM | 22.075 | 14.470 |
-+------------------------+----------+----------+
-| IQM + one-class SVM | 35.537 | 24.317 |
-+------------------------+----------+----------+
-| IQM + two-class LR | 10.184 | 30.132 |
-+------------------------+----------+----------+
-| IQM + two-class SVM | 10.527 | 21.926 |
-+------------------------+----------+----------+
-
-The ROC curves for the particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_iqm_anomaly_detection_aggr_db_ph_ph_vid.pdf>`
-
-------------
-
-
-Results for *video-video-photo* protocol
-========================================================================
-
-This section summarizes the evaluation results on the **video-video-photo** protocol of the Aggregated database for the following face PAD algorithms (for more details click on the corresponding algorithm):
-
-- :ref:`bob.pad.face.resources.face_pad.qm_one_class_gmm`,
-- :ref:`bob.pad.face.resources.face_pad.qm_one_class_svm_aggregated_db`,
-- :ref:`bob.pad.face.resources.face_pad.qm_lr`,
-- :ref:`bob.pad.face.resources.face_pad.qm_svm_aggregated_db`.
-
-For a more detailed understanding of above pipe-lines you can also check corresponding configuration files:
-
-- ``bob/pad/face/config/qm_one_class_gmm.py``,
-- ``bob/pad/face/config/qm_one_class_svm_aggregated_db.py``,
-- ``bob/pad/face/config/qm_lr.py``,
-- ``bob/pad/face/config/qm_svm_aggregated_db.py``.
-
-To run above algorithms on the :ref:`bob.pad.face.resources.databases.aggregated_db` database, using the ``video-video-photo`` protocol, execute the following:
-
-.. code-block:: sh
-
- $ spoof.py aggregated-db qm-one-class-gmm \
- --protocol video-video-photo \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_1>
-
- $ spoof.py aggregated-db qm-one-class-svm-aggregated-db \
- --protocol video-video-photo \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_2>
-
- $ spoof.py aggregated-db qm-lr \
- --protocol video-video-photo \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_3>
-
- $ spoof.py aggregated-db qm-svm-aggregated-db \
- --protocol video-video-photo \
- --sub-directory <PATH_TO_STORE_THE_RESULTS_4>
-
-.. tip::
-
- If you are in `idiap`_ you can use SGE grid to speed-up the calculations.
- Simply add ``--grid idiap`` argument to the above command. For example:
-
-
-To evaluate the results computing EER, HTER and plotting ROC you can use the
-following command:
-
-.. code-block:: sh
-
- bob pad evaluate \
- <PATH_TO_STORE_THE_RESULTS>_{1,2,3,4}/video-video-photo/scores/scores-{dev,eval} \
- --legends \
- "IQM + one-class GMM + Aggregated Db" \
- "IQM + one-class SVM + Aggregated Db" \
- "IQM + two-class LR + Aggregated Db" \
- "IQM + two-class SVM + Aggregated Db" \
- -e \
- --criterion eer \
- -o <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
-
-The EER/HTER errors for the :ref:`bob.pad.face.resources.databases.aggregated_db` database are summarized in the Table below:
-
-+------------------------+----------+----------+
-| Algorithm | EER,\% | HTER,\% |
-+========================+==========+==========+
-| IQM + one-class GMM | 13.503 | 29.794 |
-+------------------------+----------+----------+
-| IQM + one-class SVM | 18.234 | 39.502 |
-+------------------------+----------+----------+
-| IQM + two-class LR | 1.499 | 30.268 |
-+------------------------+----------+----------+
-| IQM + two-class SVM | 1.422 | 24.901 |
-+------------------------+----------+----------+
-
-The ROC curves for the particular experiment can be downloaded from here:
-
-:download:`ROC curve <img/ROC_iqm_anomaly_detection_aggr_db_vid_vid_ph.pdf>`
-
-------------
-
-
.. include:: links.rst
diff --git a/doc/resources.rst b/doc/resources.rst
index 971e96dea904df398e70508f8866ada22efe82b0..581e8cb5da61a947f96d5e5aa2a33812f5e26c04 100644
--- a/doc/resources.rst
+++ b/doc/resources.rst
@@ -43,23 +43,6 @@ Replay-Mobile Database
:members:
-.. _bob.pad.face.resources.databases.msu_mfsd:
-
-MSU MFSD Database
-================================================================================
-
-.. automodule:: bob.pad.face.config.msu_mfsd
- :members:
-
-
-.. _bob.pad.face.resources.databases.aggregated_db:
-
-Aggregated Database
-================================================================================
-
-.. automodule:: bob.pad.face.config.aggregated_db
- :members:
-
MIFS Database
================================================================================
@@ -110,33 +93,6 @@ Frame differences based features (motion analysis) + SVM for REPLAY-ATTACK
:members:
-.. _bob.pad.face.resources.face_pad.lbp_svm_aggregated_db:
-
-LBP features of facial region + SVM for Aggregated Database
-===================================================================================
-
-.. automodule:: bob.pad.face.config.lbp_svm_aggregated_db
- :members:
-
-
-.. _bob.pad.face.resources.face_pad.qm_svm_aggregated_db:
-
-Image Quality Measures as features of facial region + SVM for Aggregated Database
-===================================================================================
-
-.. automodule:: bob.pad.face.config.qm_svm_aggregated_db
- :members:
-
-
-.. _bob.pad.face.resources.face_pad.frame_diff_svm_aggregated_db:
-
-Frame differences based features (motion analysis) + SVM for Aggregated Database
-===================================================================================
-
-.. automodule:: bob.pad.face.config.frame_diff_svm_aggregated_db
- :members:
-
-
.. _bob.pad.face.resources.face_pad.qm_lr:
Image Quality Measures as features of facial region + Logistic Regression
@@ -154,13 +110,3 @@ Image Quality Measures as features of facial region + GMM-based one-class classi
.. automodule:: bob.pad.face.config.qm_one_class_gmm
:members:
-
-.. _bob.pad.face.resources.face_pad.qm_one_class_svm_aggregated_db:
-
-Image Quality Measures as features of facial region + one-class SVM classifier (anomaly detector) for Aggregated Database
-============================================================================================================================
-
-.. automodule:: bob.pad.face.config.qm_one_class_svm_aggregated_db
- :members:
-
-
diff --git a/setup.py b/setup.py
index 35e05f389bc56d1e2a9fdf0ccf3bc9e3a5e89605..a3a8e5e729f0f75f381d7e63963e47329496b83b 100644
--- a/setup.py
+++ b/setup.py
@@ -65,9 +65,7 @@ setup(
'bob.pad.database': [
'replay-attack = bob.pad.face.config.replay_attack:database',
'replay-mobile = bob.pad.face.config.replay_mobile:database',
- 'msu-mfsd = bob.pad.face.config.msu_mfsd:database',
'casiafasd = bob.pad.face.config.casiafasd:database',
- 'aggregated-db = bob.pad.face.config.aggregated_db:database',
'mifs = bob.pad.face.config.mifs:database',
'batl-db = bob.pad.face.config.database.batl.batl_db:database',
'batl-db-infrared = bob.pad.face.config.database.batl.batl_db_infrared:database',
@@ -86,9 +84,7 @@ setup(
# databases
'replay-attack = bob.pad.face.config.replay_attack',
'replay-mobile = bob.pad.face.config.replay_mobile',
- 'msu-mfsd = bob.pad.face.config.msu_mfsd',
'casiafasd = bob.pad.face.config.casiafasd',
- 'aggregated-db = bob.pad.face.config.aggregated_db',
'mifs = bob.pad.face.config.mifs',
'batl-db = bob.pad.face.config.database.batl.batl_db',
'batl-db-infrared = bob.pad.face.config.database.batl.batl_db_infrared',
@@ -100,22 +96,15 @@ setup(
# baselines using SVM:
'lbp-svm = bob.pad.face.config.lbp_svm',
- 'lbp-svm-aggregated-db = bob.pad.face.config.lbp_svm_aggregated_db',
'qm-svm = bob.pad.face.config.qm_svm',
- 'qm-svm-aggregated-db = bob.pad.face.config.qm_svm_aggregated_db',
'frame-diff-svm = bob.pad.face.config.frame_diff_svm',
- 'frame-diff-svm-aggregated-db = bob.pad.face.config.frame_diff_svm_aggregated_db',
-
- # baselines using one-class SVM
- 'qm-one-class-svm-aggregated-db = bob.pad.face.config.qm_one_class_svm_aggregated_db',
- 'qm-one-class-svm-cascade-aggregated-db = bob.pad.face.config.qm_one_class_svm_cascade_aggregated_db',
# baselines using LR:
- 'qm-lr = bob.pad.face.config.qm_lr', # this pipe-line can be used both for individual and Aggregated databases.
+ 'qm-lr = bob.pad.face.config.qm_lr',
'lbp-lr-batl-D-T-IR = bob.pad.face.config.lbp_lr_batl_D_T_IR', # this pipe-line can be used both for BATL databases, Depth, Thermal and Infrared channels.
# baselines using GMM:
- 'qm-one-class-gmm = bob.pad.face.config.qm_one_class_gmm', # this pipe-line can be used both for individual and Aggregated databases.
+ 'qm-one-class-gmm = bob.pad.face.config.qm_one_class_gmm',
],
# registered preprocessors:
diff --git a/test-requirements.txt b/test-requirements.txt
index fae37ea3fb6ed9f6464be836218e5eb9547cc4d6..aa9448073ae335e55e6249104f3bec8128f27fcf 100644
--- a/test-requirements.txt
+++ b/test-requirements.txt
@@ -1,7 +1,6 @@
nose
bob.db.replay
bob.db.replaymobile
-bob.db.msu_mfsd_mod
bob.db.casia_fasd
bob.db.mobio
bob.db.maskattack