diff --git a/bob/pad/face/algorithm/VideoSvmPadAlgorithm.py b/bob/pad/face/algorithm/VideoSvmPadAlgorithm.py
index e99f7572ee5560bc808643c86c96534569faf9e6..110c07866f644525d86cf6385d13416a602069ad 100644
--- a/bob/pad/face/algorithm/VideoSvmPadAlgorithm.py
+++ b/bob/pad/face/algorithm/VideoSvmPadAlgorithm.py
@@ -137,9 +137,9 @@ class VideoSvmPadAlgorithm(Algorithm):
**Parameters:**
- ``frame_containers`` : :py:class:`list`
- Each element in the list is a Frame Container, , see ``bob.bio.video.utils.FrameContainer``.
- Each frame Container conteins feature vectors for the particular individual/person.
+ ``frame_containers`` : [FrameContainer]
+ A list of Frame Containers, , see ``bob.bio.video.utils.FrameContainer``.
+ Each frame Container contains feature vectors for the particular individual/person.
**Returns:**
@@ -173,8 +173,8 @@ class VideoSvmPadAlgorithm(Algorithm):
**Returns:**
- ``combinations`` : :py:class:`list`
- List of dictionaries containing the combinations.
+ ``combinations`` : [:py:class:`dict`]
+ A list of dictionaries containing the combinations.
"""
varNames = sorted(input_dict)
@@ -257,10 +257,10 @@ class VideoSvmPadAlgorithm(Algorithm):
**Parameters:**
- ``training_features`` : :py:class:`list`
+ ``training_features`` : [[FrameContainer], [FrameContainer]]
A list containing two elements: [0] - a list of Frame Containers with
- feature vectors fot the real class; [1] - a list of Frame Containers with
- feature vectors fot the attack class.
+ feature vectors for the real class; [1] - a list of Frame Containers with
+ feature vectors for the attack class.
``n_samples`` : :py:class:`int`
Number of uniformly selected feature vectors per class.
@@ -457,10 +457,10 @@ class VideoSvmPadAlgorithm(Algorithm):
**Parameters:**
- ``training_features`` : :py:class:`list`
+ ``training_features`` : [[FrameContainer], [FrameContainer]]
A list containing two elements: [0] - a list of Frame Containers with
- feature vectors fot the real class; [1] - a list of Frame Containers with
- feature vectors fot the attack class.
+ feature vectors for the real class; [1] - a list of Frame Containers with
+ feature vectors for the attack class.
``n_samples`` : :py:class:`int`
Number of uniformly selected feature vectors per class defining the
@@ -589,10 +589,10 @@ class VideoSvmPadAlgorithm(Algorithm):
**Parameters:**
- ``training_features`` : :py:class:`list`
+ ``training_features`` : [[FrameContainer], [FrameContainer]]
A list containing two elements: [0] - a list of Frame Containers with
- feature vectors fot the real class; [1] - a list of Frame Containers with
- feature vectors fot the attack class.
+ feature vectors for the real class; [1] - a list of Frame Containers with
+ feature vectors for the attack class.
``projector_file`` : :py:class:`str`
The file to save the trained projector to.
@@ -660,16 +660,14 @@ class VideoSvmPadAlgorithm(Algorithm):
An array containing class probabilities for each frame.
First column contains probabilities for each frame being a real class.
Second column contains probabilities for each frame being an attack class.
- Must be writable with the :py:meth:`write_feature` function and
- readable with the :py:meth:`read_feature` function.
+ Must be writable with the ``write_feature`` function and
+ readable with the ``read_feature`` function.
"""
features_array = self.convert_frame_cont_to_array(feature)
probabilities = self.machine.predict_class_and_probabilities(features_array)[1]
-# probabilities = self.machine.predict_class_and_scores(features_array)[1]
-
return probabilities
@@ -716,12 +714,10 @@ class VideoSvmPadAlgorithm(Algorithm):
**Returns:**
- ``list_of_scores`` : list
+ ``list_of_scores`` : [:py:class:`float`]
A list containing the scores.
"""
-# import ipdb; ipdb.set_trace()
-
if self.frame_level_scores_flag:
list_of_scores = self.score(toscore)
@@ -733,53 +729,3 @@ class VideoSvmPadAlgorithm(Algorithm):
return list_of_scores
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/bob/pad/face/config/database/replay.py b/bob/pad/face/config/database/replay.py
index f1cbd70cb6725aa1ff17a172456eecd24d6c8f70..197355ceb13f6d0027948829cd2fbee0d60e1c37 100644
--- a/bob/pad/face/config/database/replay.py
+++ b/bob/pad/face/config/database/replay.py
@@ -1,11 +1,25 @@
#!/usr/bin/env python
+"""`Replayattack`_ is a database for face PAD experiments.
+
+The Replay-Attack Database for face spoofing consists of 1300 video clips of photo and video attack attempts to 50 clients,
+under different lighting conditions. This Database was produced at the Idiap Research Institute, in Switzerland.
+The reference citation is [CAM12]_.
+
+You can download the raw data of the `Replayattack`_ database by following
+the link.
+
+.. include:: links.rst
+"""
+
from bob.pad.face.database import ReplayPadDatabase
# 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_REPLAY_ATTACK_DIRECTORY]"
+"""Value of ``~/.bob_bio_databases.txt`` for this database"""
+
original_extension = ".mov" # extension of the data files
@@ -15,3 +29,17 @@ database = ReplayPadDatabase(
original_extension=original_extension,
training_depends_on_protocol=True,
)
+"""The :py:class:`bob.pad.base.database.PadDatabase` derivative with Replayattack
+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_REPLAY_ATTACK_DIRECTORY]``.
+You must make sure to create ``${HOME}/.bob_bio_databases.txt`` setting this
+value to the place where you actually installed the Replayattack Database, as
+explained in the section :ref:`bob.pad.face.baselines`.
+"""
\ No newline at end of file
diff --git a/bob/pad/face/config/extractor/frame_diff_features.py b/bob/pad/face/config/extractor/frame_diff_features.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0e6fb398d4a216ed90877d286b42599793eda02
--- /dev/null
+++ b/bob/pad/face/config/extractor/frame_diff_features.py
@@ -0,0 +1,19 @@
+#!/usr/bin/env python
+
+from bob.pad.face.extractor import FrameDiffFeatures
+
+
+#=======================================================================================
+# Define instances here:
+
+window_size=20
+overlap=0
+
+frame_diff_feat_extr_w20_over0 = FrameDiffFeatures(window_size=window_size,
+ overlap=overlap)
+
+window_size=100
+
+frame_diff_feat_extr_w100_over0 = FrameDiffFeatures(window_size=window_size,
+ overlap=overlap)
+
diff --git a/bob/pad/face/config/frame_diff_svm.py b/bob/pad/face/config/frame_diff_svm.py
new file mode 100644
index 0000000000000000000000000000000000000000..d55a46237352fd68f936f9d4f43a8aec7281a0a3
--- /dev/null
+++ b/bob/pad/face/config/frame_diff_svm.py
@@ -0,0 +1,96 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+
+"""
+@author: Olegs Nikisins
+
+This file contains configurations to run Frame Differences and SVM based face PAD baseline.
+The settings are tuned for the Replay-attack database.
+The idea of the algorithms is inherited from the following paper: [AM11]_.
+"""
+
+
+#=======================================================================================
+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
+CHECK_FACE_SIZE_FLAG = True # Check size of the face
+MIN_FACE_SIZE = 50 # Minimal size of the face to consider
+
+preprocessor = FrameDifference(number_of_frames = NUMBER_OF_FRAMES,
+ check_face_size_flag = CHECK_FACE_SIZE_FLAG,
+ 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 ..algorithm import VideoSvmPadAlgorithm
+
+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
+
+algorithm = VideoSvmPadAlgorithm(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)
+"""
+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 data is also mean-std normalized, ``mean_std_norm_flag = True``.
+"""
+
+
+
+
+
diff --git a/bob/pad/face/database/replay.py b/bob/pad/face/database/replay.py
index e8f052f2a725f6c8da4172f52ca92636afe4e3b0..fd8bb048677703d1675c44eedce1d732c8161cdf 100644
--- a/bob/pad/face/database/replay.py
+++ b/bob/pad/face/database/replay.py
@@ -1,12 +1,6 @@
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
-"""
-Created on Thu May 4 12:03:36 2017
-High level implementation for the REPLAY-ATTACK database
-
-@author: Olegs Nikisins <olegs.nikisins@idiap.ch>
-"""
#==============================================================================
diff --git a/bob/pad/face/extractor/FrameDiffFeatures.py b/bob/pad/face/extractor/FrameDiffFeatures.py
new file mode 100644
index 0000000000000000000000000000000000000000..8560a5fe6257754efa0dbae68bdc1d2a6b8f7e99
--- /dev/null
+++ b/bob/pad/face/extractor/FrameDiffFeatures.py
@@ -0,0 +1,326 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jun 14 10:13:21 2017
+
+@author: Olegs Nikisins
+"""
+
+#==============================================================================
+# Import what is needed here:
+
+from bob.bio.base.extractor import Extractor
+
+import numpy as np
+
+import sys
+
+import bob.bio.video
+
+#==============================================================================
+# Main body:
+
+class FrameDiffFeatures(Extractor):
+ """
+ This class is designed to extract features describing frame differences.
+
+ The class allows to compute the following features in the window of the
+ length defined by ``window_size`` argument:
+
+ 1. The minimum value observed on the cluster
+ 2. The maximum value observed on the cluster
+ 3. The mean value observed
+ 4. The standard deviation on the cluster (unbiased estimator)
+ 5. The DC ratio (D) as defined by:
+
+ .. math::
+
+ D(N) = (\sum_{i=1}^N{|FFT_i|}) / (|FFT_0|)
+
+ **Parameters:**
+
+ ``window_size`` : :py:class:`int`
+ The size of the window to use for feature computation.
+
+ ``overlap`` : :py:class:`int`
+ Determines the window overlapping; this number has to be between
+ 0 (no overlapping) and 'window-size'-1. Default: 0.
+ """
+
+ def __init__(self,
+ window_size,
+ overlap = 0):
+
+ Extractor.__init__(self,
+ window_size = window_size,
+ overlap = overlap)
+
+ self.window_size = window_size
+ self.overlap = overlap
+
+
+ #==========================================================================
+ def dcratio(self, arr):
+ """
+ Calculates the DC ratio as defined by the following formula:
+
+ .. math::
+
+ D(N) = (\sum_{i=1}^N{|FFT_i|}) / (|FFT_0|)
+
+ **Parameters:**
+
+ ``arr`` : 1D :py:class:`numpy.ndarray`
+ A 1D array containg frame differences.
+
+ **Returns:**
+
+ ``dcratio`` : :py:class:`float`
+ Calculated DC ratio.
+ """
+
+ if arr.shape[0] <= 1:
+ return 0.
+
+ res = np.fft.fft(arr.astype('complex128'))
+ res = np.absolute(res) # absolute value
+
+ if res[0] == 0:
+ s = sum(res[1:])
+ if s > 0:
+ return sys.float_info.max
+ elif s < 0:
+ return -sys.float_info.max
+ else:
+ return 0
+
+ dcratio = sum(res[1:]) / res[0]
+
+ return dcratio
+
+
+ #==========================================================================
+ def remove_nan_rows(self, data):
+ """
+ This function removes rows of nan's from the input array. If the input
+ array contains nan's only, then an array of ones of the size
+ (1 x n_features) is returned.
+
+ **Parameters:**
+
+ ``data`` : 2D :py:class:`numpy.ndarray`
+ An input array of features. Rows - samples, columns - features.
+
+ **Returns:**
+
+ ``ret_arr`` : 2D :py:class:`numpy.ndarray`
+ Array of features without nan samples. Rows - samples, columns - features.
+ """
+
+ d = np.vstack(data)
+
+ ret_arr = d[~np.isnan(d.sum(axis=1)), :]
+
+ if ret_arr.shape[0] == 0: # if array is empty, return array of ones
+
+ ret_arr = np.ones((1, ret_arr.shape[1]))
+
+ return ret_arr
+
+
+ #==========================================================================
+ def cluster_5quantities(self, arr, window_size, overlap):
+ """
+ Calculates the clustered values as described at the paper: Counter-
+ Measures to Photo Attacks in Face Recognition: a public database and a
+ baseline, Anjos & Marcel, IJCB'11.
+
+ This script will output a number of clustered observations containing the 5
+ described quantities for windows of a configurable size (N):
+
+ 1. The minimum value observed on the cluster
+ 2. The maximum value observed on the cluster
+ 3. The mean value observed
+ 4. The standard deviation on the cluster (unbiased estimator)
+ 5. The DC ratio (D) as defined by:
+
+ .. math::
+
+ D(N) = (\sum_{i=1}^N{|FFT_i|}) / (|FFT_0|)
+
+ .. note::
+
+ We always ignore the first entry from the input array as, by
+ definition, it is always zero.
+
+ **Parameters:**
+
+ ``arr`` : 1D :py:class:`numpy.ndarray`
+ A 1D array containg frame differences.
+
+ ``window_size`` : :py:class:`int`
+ The size of the window to use for feature computation.
+
+ ``overlap`` : :py:class:`int`
+ Determines the window overlapping; this number has to be between
+ 0 (no overlapping) and 'window-size'-1.
+
+ **Returns:**
+
+ ``retval`` : 2D :py:class:`numpy.ndarray`
+ Array of features without nan samples. Rows - samples, columns - features.
+ Here sample corresponds to features computed from the particular
+ window of the length ``window_size``.
+ """
+
+ retval = np.ndarray((arr.shape[0], 5), dtype='float64')
+ retval[:] = np.NaN
+
+ for k in range(0, arr.shape[0] - window_size + 1, window_size - overlap):
+
+ obs = arr[k:k + window_size].copy()
+
+ # replace NaN values by set mean so they don't disturb calculations
+ # much
+ ok = obs[~np.isnan(obs)]
+
+ obs[np.isnan(obs)] = ok.mean()
+
+ retval[k + window_size - 1] = \
+ (obs.min(), obs.max(), obs.mean(), obs.std(ddof=1), self.dcratio(obs))
+
+ retval = self.remove_nan_rows(retval) # clean-up nan's in the array
+
+ return retval
+
+
+ #==========================================================================
+ def convert_arr_to_frame_cont(self, data):
+ """
+ This function converts an array of samples into a FrameContainer, where
+ each frame stores features of a particular sample.
+
+ **Parameters:**
+
+ ``data`` : 2D :py:class:`numpy.ndarray`
+ An input array of features of the size
+ (Nr. of samples X Nr. of features).
+
+ **Returns:**
+
+ ``frames`` : FrameContainer
+ Resulting FrameContainer, where each frame stores features of
+ a particular sample.
+ """
+
+ frames = bob.bio.video.FrameContainer() # initialize the FrameContainer
+
+ for idx, sample in enumerate(data):
+
+ frames.add(idx, sample)
+
+ return frames
+
+
+ #==========================================================================
+ def comp_features(self, data, window_size, overlap):
+ """
+ This function computes features for frame differences in the facial and
+ non-facial regions.
+
+ **Parameters:**
+
+ ``data`` : 2D :py:class:`numpy.ndarray`
+ An input array of frame differences in facial and non-facial regions.
+ The first column contains frame differences of facial regions.
+ The second column contains frame differences of non-facial/background regions.
+
+ ``window_size`` : :py:class:`int`
+ The size of the window to use for feature computation.
+
+ ``overlap`` : :py:class:`int`
+ Determines the window overlapping; this number has to be between
+ 0 (no overlapping) and 'window-size'-1. Default: 0.
+
+ **Returns:**
+
+ ``frames`` : FrameContainer
+ Features describing frame differences, stored in the FrameContainer.
+ """
+
+ d_face = self.cluster_5quantities( data[:, 0], window_size, overlap )
+
+ d_bg = self.cluster_5quantities( data[:, 1], window_size, overlap )
+
+ features = np.hstack((d_face, d_bg))
+
+ frames = self.convert_arr_to_frame_cont(features)
+
+ return frames
+
+
+ #==========================================================================
+ def __call__(self, data):
+ """
+ This function computes features for frame differences in the facial and
+ non-facial regions.
+
+ **Parameters:**
+
+ ``data`` : 2D :py:class:`numpy.ndarray`
+ An input array of frame differences in facial and non-facial regions.
+ The first column contains frame differences of facial regions.
+ The second column contains frame differences of non-facial/background regions.
+
+ **Returns:**
+
+ ``frames`` : FrameContainer
+ Features describing frame differences, stored in the FrameContainer.
+ """
+
+ frames = self.comp_features(data, self.window_size, self.overlap)
+
+ return frames
+
+
+ #==========================================================================
+ def write_feature(self, frames, file_name):
+ """
+ Writes the given data (that has been generated using the __call__ function of this class) to file.
+ This method overwrites the write_data() method of the Extractor class.
+
+ **Parameters:**
+
+ ``frames`` :
+ Data returned by the __call__ method of the class.
+
+ ``file_name`` : :py:class:`str`
+ Name of the file.
+ """
+
+ bob.bio.video.extractor.Wrapper(Extractor()).write_feature(frames, file_name)
+
+
+ #==========================================================================
+ def read_feature(self, file_name):
+ """
+ Reads the preprocessed data from file.
+ This method overwrites the read_data() method of the Extractor class.
+
+ **Parameters:**
+
+ ``file_name`` : :py:class:`str`
+ Name of the file.
+
+ **Returns:**
+
+ ``frames`` : :py:class:`bob.bio.video.FrameContainer`
+ Frames stored in the frame container.
+ """
+
+ frames = bob.bio.video.extractor.Wrapper(Extractor()).read_feature(file_name)
+
+ return frames
+
+
+
diff --git a/bob/pad/face/extractor/LBPHistogram.py b/bob/pad/face/extractor/LBPHistogram.py
index ee0a583b57f225dbc89e084d941842364171ddb3..a1b15e16a5cc272599b13f9051a30ac8e3ed5547 100644
--- a/bob/pad/face/extractor/LBPHistogram.py
+++ b/bob/pad/face/extractor/LBPHistogram.py
@@ -7,7 +7,7 @@ import numpy
class LBPHistogram(Extractor):
"""Calculates a normalized LBP histogram over an image.
- These features are implemented based on [ChingovskaEffectivnes12]_.
+ These features are implemented based on [CAM12]_.
Parameters
----------
diff --git a/bob/pad/face/extractor/__init__.py b/bob/pad/face/extractor/__init__.py
index 18f4b835c9389d6486d8e548a817784646fbb339..310989b454bdd1ebb73ef43b6c4f4a9fd56a72e6 100644
--- a/bob/pad/face/extractor/__init__.py
+++ b/bob/pad/face/extractor/__init__.py
@@ -3,6 +3,7 @@ from .VideoLBPHistogram import VideoLBPHistogram
from .ImageQualityMeasure import ImageQualityMeasure
from .VideoDataLoader import VideoDataLoader
from .VideoQualityMeasure import VideoQualityMeasure
+from .FrameDiffFeatures import FrameDiffFeatures
def __appropriate__(*args):
"""Says object was actually declared here, and not in the import module.
@@ -28,5 +29,6 @@ __appropriate__(
ImageQualityMeasure,
VideoQualityMeasure,
VideoDataLoader,
+ FrameDiffFeatures,
)
__all__ = [_ for _ in dir() if not _.startswith('_')]
diff --git a/bob/pad/face/preprocessor/VideoFaceCrop.py b/bob/pad/face/preprocessor/VideoFaceCrop.py
index 3ea266c98fece64d37d3d751c5c14ad894678ef1..02a4371afec104036954e946bd02fd68ae9b44f1 100644
--- a/bob/pad/face/preprocessor/VideoFaceCrop.py
+++ b/bob/pad/face/preprocessor/VideoFaceCrop.py
@@ -77,7 +77,7 @@ class VideoFaceCrop(Preprocessor, object):
Default: ``False``.
``kwargs``
- Remaining keyword parameters passed to the :py:class:`Base` constructor, such as ``color_channel`` or ``dtype``.
+ Remaining keyword parameters passed to the Base constructor, such as ``color_channel`` or ``dtype``.
"""
#==========================================================================
diff --git a/doc/api.rst b/doc/api.rst
new file mode 100644
index 0000000000000000000000000000000000000000..4d29355e650ea5fb22acf9ce2fe438ddd99b2df4
--- /dev/null
+++ b/doc/api.rst
@@ -0,0 +1,37 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.pad.face.api:
+
+============
+ Python API
+============
+
+This section lists all the functionality available in this library allowing to run face PAD experiments.
+
+
+Database Interfaces
+------------------------------
+
+
+REPLAY-ATTACK Database
+========================
+
+.. automodule:: bob.pad.face.database.replay
+
+
+Pre-processors
+------------------------------
+
+.. automodule:: bob.pad.face.preprocessor
+
+
+Feature Extractors
+------------------------------
+
+.. automodule:: bob.pad.face.extractor
+
+
+Matching Algorithms
+------------------------------
+
+.. automodule:: bob.pad.face.algorithm
\ No newline at end of file
diff --git a/doc/baselines.rst b/doc/baselines.rst
index 7569495f0bd047988573b14191e8ef71ef49a4d2..a891a61ae6a1ca10556eec3ba3057d7a21b86b4c 100644
--- a/doc/baselines.rst
+++ b/doc/baselines.rst
@@ -193,4 +193,52 @@ The ROC curves for the particular experiment can be downloaded from here:
------------
+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`.
+
+To run this baseline on the `replayattack`_ database, using the ``grandtest`` protocol, execute the following:
+
+.. code-block:: sh
+
+ $ ./bin/spoof.py frame-diff-svm \
+ --database replay --protocol grandtest --groups train dev eval \
+ --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
+
+ ./bin/evaluate.py \
+ --dev-files <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-dev \
+ --eval-files <PATH_TO_STORE_THE_RESULTS>/grandtest/scores/scores-eval \
+ --legends "10 features for each window in Frame Differences + SVM classifier + REPLAY-ATTACK database" \
+ -F 7 \
+ --criterion EER \
+ --roc <PATH_TO_STORE_THE_RESULTS>/ROC.pdf
+
+The EER/HTER errors for `replayattack`_ database are summarized in the Table below:
+
++-------------------+----------+----------+
+| Protocol | EER,\% | HTER,\% |
++===================+==========+==========+
+| ``grandtest`` | 11.752 | 13.195 |
++-------------------+----------+----------+
+
+The ROC curves for the particular experiment can be downloaded from here:
+
+:download:`ROC curve <img/ROC_frame_diff_svm_replay_attack.pdf>`
+
+------------
+
+
.. include:: links.rst
diff --git a/doc/img/ROC_frame_diff_svm_replay_attack.pdf b/doc/img/ROC_frame_diff_svm_replay_attack.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..a04d2b6c36a094b8ac1d941c812c8e724c0c083c
Binary files /dev/null and b/doc/img/ROC_frame_diff_svm_replay_attack.pdf differ
diff --git a/doc/index.rst b/doc/index.rst
index a260d13f553315f56fd1407ed957d81afaa60135..fda461eacc018b1a0541c6b1dcb084586916eee3 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -24,7 +24,7 @@ Users Guide
baselines
references
resources
- py_api
+ api
.. todolist::
diff --git a/doc/py_api.rst b/doc/py_api.rst
deleted file mode 100644
index be29aac297535990753e19e6adb5ae437289a09c..0000000000000000000000000000000000000000
--- a/doc/py_api.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-.. vim: set fileencoding=utf-8 :
-
-.. _bob.pad.face.py_api:
-
-===================================
- Tools implemented in bob.pad.face
-===================================
-
-This section listst all the functionality available in this library allowing to run face PAD experiments.
-
-Databases
-===================================
-
-Image Preprocessors
-===================================
-
-Video Preprocessors
-===================================
-
-Image Extractors
-===================================
-
-Video Extractors
-===================================
-
-Image Extractors
-===================================
-
-
diff --git a/doc/references.rst b/doc/references.rst
index 911101849170b2dd52a7cd6d1a885887bf7b9166..9b34385ac989b36a6792fcf40dc75187603218b6 100644
--- a/doc/references.rst
+++ b/doc/references.rst
@@ -12,3 +12,6 @@ References
.. [CBVM16] *A. Costa-Pazo, S. Bhattacharjee, E. Vazquez-Fernandez and S. Marcel*, **The Replay-Mobile Face Presentation-Attack Database**,
in: Biometrics Special Interest Group (BIOSIG), 2016 BIOSIG - Proceedings of the International Conference of the, 2016, pp. 1-7.
+
+.. [AM11] *A. Anjos and S. Marcel*, **Counter-measures to photo attacks in face recognition: A public database and a baseline**,
+ in: 2011 International Joint Conference on Biometrics (IJCB), Washington, DC, 2011, pp. 1-7.
diff --git a/doc/resources.rst b/doc/resources.rst
index 7b9d574bc3e99c013c3cac8dc84f8d436745dbec..f943abba35447a02e73dca63e70b6be90bfce79a 100644
--- a/doc/resources.rst
+++ b/doc/resources.rst
@@ -10,19 +10,27 @@ This section contains a listing of all ready-to-use resources you can find in
this package.
+---------------------------------
+
.. _bob.pad.face.resources.databases:
Databases
------------
+These configuration files/resources contain entry points for the ``--database`` command line argument of the
+``spoof.py`` script.
+.. _bob.pad.face.resources.databases.replay:
+Replay-attack Database
+================================================================================
+.. automodule:: bob.pad.face.config.database.replay
+ :members:
-
-
+---------------------------------
.. _bob.pad.face.resources.face_pad:
@@ -42,7 +50,7 @@ The configuration files contain at least the following arguments of the ``spoof.
.. _bob.pad.face.resources.face_pad.lbp_svm_replayattack:
LBP features of facial region + SVM for REPLAY-ATTACK
-======================================================
+================================================================================
.. automodule:: bob.pad.face.config.lbp_svm
:members:
@@ -54,4 +62,13 @@ Image Quality Measures as features of facial region + SVM for REPLAY-ATTACK
================================================================================
.. automodule:: bob.pad.face.config.qm_svm
- :members:
\ No newline at end of file
+ :members:
+
+
+.. _bob.pad.face.resources.face_pad.frame_diff_svm_replayattack:
+
+Frame differences based features (motion analysis) + SVM for REPLAY-ATTACK
+================================================================================
+
+.. automodule:: bob.pad.face.config.frame_diff_svm
+ :members:
diff --git a/setup.py b/setup.py
index 338b4dcfe4b03fda511137b5ae065a7604822587..13ada3f681ca3b229b4a059ed700f8b52a976cd6 100644
--- a/setup.py
+++ b/setup.py
@@ -104,6 +104,7 @@ setup(
# baselines:
'lbp-svm = bob.pad.face.config.lbp_svm',
'qm-svm = bob.pad.face.config.qm_svm',
+ 'frame-diff-svm = bob.pad.face.config.frame_diff_svm',
],
# registered preprocessors:
@@ -120,6 +121,8 @@ setup(
'bob.pad.extractor': [
'video-lbp-histogram-extractor-n8r1-uniform = bob.pad.face.config.extractor.video_lbp_histogram:video_lbp_histogram_extractor_n8r1_uniform',
'video-quality-measure-galbally-msu = bob.pad.face.config.extractor.video_quality_measure:video_quality_measure_galbally_msu',
+ 'frame-diff-feat-extr-w20-over0 = bob.pad.face.config.extractor.frame_diff_features:frame_diff_feat_extr_w20_over0',
+ 'frame-diff-feat-extr-w100-over0 = bob.pad.face.config.extractor.frame_diff_features:frame_diff_feat_extr_w100_over0',
],
# registered algorithms: