Merge branch 'rebase_branch' into 'master'

Docs on Anomaly detection based PAD using Aggregated Db + Sparse coding stuff

See merge request !15

bob/pad/face/config/extractor/video_hist_of_sparse_codes.py

+#!/usr/bin/env python
+
+from bob.pad.face.extractor import VideoHistOfSparseCodes
+
+
+#=======================================================================================
+# Define instances here:
+
+METHOD = "mean"
+extractor_mean = VideoHistOfSparseCodes(method = METHOD)
+
+
+METHOD = "hist"
+extractor_hist = VideoHistOfSparseCodes(method = METHOD)
+
+

bob/pad/face/config/preprocessor/video_sparse_coding.py

+#!/usr/bin/env python
+
+from bob.pad.face.preprocessor import VideoSparseCoding
+
+
+#=======================================================================================
+# Define instances here:
+
+BLOCK_SIZE = 5
+BLOCK_LENGTH = 10
+MIN_FACE_SIZE = 50
+NORM_FACE_SIZE = 64
+DICTIONARY_FILE_NAMES = ["/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_front.hdf5",
+                         "/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_hor.hdf5",
+                         "/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_vert.hdf5"]
+FRAME_STEP = 10
+
+preprocessor = VideoSparseCoding(gblock_size = BLOCK_SIZE,
+                                 block_length = BLOCK_LENGTH,
+                                 min_face_size = MIN_FACE_SIZE,
+                                 norm_face_size = NORM_FACE_SIZE,
+                                 dictionary_file_names = DICTIONARY_FILE_NAMES,
+                                 frame_step = FRAME_STEP)
+
+
+#=======================================================================================
+
+
+BLOCK_SIZE = 5
+BLOCK_LENGTH = 10
+MIN_FACE_SIZE = 50
+NORM_FACE_SIZE = 64
+DICTIONARY_FILE_NAMES = ["/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_front_10_5_16.hdf5",
+                         "/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_hor_10_5_16.hdf5",
+                         "/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_vert_10_5_16.hdf5"]
+FRAME_STEP = 2
+EXTRACT_HISTOGRAMS_FLAG = True
+
+preprocessor_10_5_16 = VideoSparseCoding(gblock_size = BLOCK_SIZE,
+                                         block_length = BLOCK_LENGTH,
+                                         min_face_size = MIN_FACE_SIZE,
+                                         norm_face_size = NORM_FACE_SIZE,
+                                         dictionary_file_names = DICTIONARY_FILE_NAMES,
+                                         frame_step = FRAME_STEP,
+                                         extract_histograms_flag = EXTRACT_HISTOGRAMS_FLAG)
+
+BLOCK_SIZE = 5
+BLOCK_LENGTH = 10
+MIN_FACE_SIZE = 50
+NORM_FACE_SIZE = 64
+DICTIONARY_FILE_NAMES = ["/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_front_10_5_32.hdf5",
+                         "/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_hor_10_5_32.hdf5",
+                         "/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_vert_10_5_32.hdf5"]
+FRAME_STEP = 2
+EXTRACT_HISTOGRAMS_FLAG = True
+
+preprocessor_10_5_32 = VideoSparseCoding(gblock_size = BLOCK_SIZE,
+                                         block_length = BLOCK_LENGTH,
+                                         min_face_size = MIN_FACE_SIZE,
+                                         norm_face_size = NORM_FACE_SIZE,
+                                         dictionary_file_names = DICTIONARY_FILE_NAMES,
+                                         frame_step = FRAME_STEP,
+                                         extract_histograms_flag = EXTRACT_HISTOGRAMS_FLAG)
+
+BLOCK_SIZE = 5
+BLOCK_LENGTH = 10
+MIN_FACE_SIZE = 50
+NORM_FACE_SIZE = 64
+DICTIONARY_FILE_NAMES = ["/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_front_10_5_64.hdf5",
+                         "/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_hor_10_5_64.hdf5",
+                         "/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_vert_10_5_64.hdf5"]
+FRAME_STEP = 2
+EXTRACT_HISTOGRAMS_FLAG = True
+
+preprocessor_10_5_64 = VideoSparseCoding(gblock_size = BLOCK_SIZE,
+                                         block_length = BLOCK_LENGTH,
+                                         min_face_size = MIN_FACE_SIZE,
+                                         norm_face_size = NORM_FACE_SIZE,
+                                         dictionary_file_names = DICTIONARY_FILE_NAMES,
+                                         frame_step = FRAME_STEP,
+                                         extract_histograms_flag = EXTRACT_HISTOGRAMS_FLAG)
+
+
+BLOCK_SIZE = 5
+BLOCK_LENGTH = 10
+MIN_FACE_SIZE = 50
+NORM_FACE_SIZE = 64
+DICTIONARY_FILE_NAMES = ["/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_front_10_5_128.hdf5",
+                         "/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_hor_10_5_128.hdf5",
+                         "/idiap/user/onikisins/Projects/ODIN/Python/scripts/test_scripts/data/dictionary_vert_10_5_128.hdf5"]
+FRAME_STEP = 2
+EXTRACT_HISTOGRAMS_FLAG = True
+
+preprocessor_10_5_128 = VideoSparseCoding(gblock_size = BLOCK_SIZE,
+                                         block_length = BLOCK_LENGTH,
+                                         min_face_size = MIN_FACE_SIZE,
+                                         norm_face_size = NORM_FACE_SIZE,
+                                         dictionary_file_names = DICTIONARY_FILE_NAMES,
+                                         frame_step = FRAME_STEP,
+                                         extract_histograms_flag = EXTRACT_HISTOGRAMS_FLAG)
+
+
+
+
+
+

bob/pad/face/extractor/VideoHistOfSparseCodes.py

+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Sep 27 11:33:45 2017
+
+@author: Olegs Nikisins
+"""
+
+#==============================================================================
+# Import what is needed here:
+
+from bob.bio.base.extractor import Extractor
+
+import numpy as np
+
+import bob.bio.video
+
+from bob.pad.face.extractor import ImageQualityMeasure
+
+#==============================================================================
+# Main body:
+
+class VideoHistOfSparseCodes(Extractor, object):
+    """
+    This class is designed to extract histograms of sparse codes.
+
+    **Parameters:**
+
+    ``method`` : :py:class:`str`
+        A method to use in the histogram computation. Two options are available:
+        "mean" and "hist". Default: "mean".
+    """
+
+    #==========================================================================
+    def __init__(self,
+                 method = "mean"):
+
+        super(VideoHistOfSparseCodes, self).__init__(method = method)
+
+        self.method = method
+
+        # extractor to process a single image/frame:
+        extractor = ImageQualityMeasure()
+
+        # This extractor is used only to write and read the feature vectors.
+        self.video_extractor = bob.bio.video.extractor.Wrapper(extractor)
+
+
+    #==========================================================================
+    def comp_hist_of_sparse_codes(self, frames, method):
+        """
+        Compute the histograms of sparse codes.
+        """
+
+        histograms = []
+
+        for frame_data in frames:
+
+            frame = frame_data[1]
+
+            if method == "mean":
+
+                frame_codes = np.mean(frame, axis=1)
+
+            if method == "hist":
+
+                frame_codes = np.mean(frame!=0, axis=1)
+
+            for idx, row in enumerate(frame_codes):
+
+                frame_codes[idx,:] = row/np.sum(row)
+
+            hist = frame_codes.flatten()
+
+            histograms.append(hist)
+
+        return histograms
+
+
+    #==========================================================================
+    def convert_sparse_codes_to_frame_container(self, list_of_arrays):
+        """
+        Convert an input list of arrays into Frame Container.
+
+        **Parameters:**
+
+        ``list_of_arrays`` : [:py:class:`numpy.ndarray`]
+            A list of arrays.
+
+        **Returns:**
+
+        ``frame_container`` : FrameContainer
+            FrameContainer containing the feature vectors.
+        """
+
+        frame_container = bob.bio.video.FrameContainer() # initialize the FrameContainer
+
+        for idx, item in enumerate(list_of_arrays):
+
+            frame_container.add(idx, item) # add frame to FrameContainer
+
+        return frame_container
+
+
+    #==========================================================================
+    def __call__(self, frames):
+        """
+        Extract feature vectors.
+
+        **Parameters:**
+
+        ``frames`` : FrameContainer or string.
+            Data stored in the FrameContainer,
+            see ``bob.bio.video.utils.FrameContainer`` for further details.
+
+        **Returns:**
+
+        ``frame_container`` : FrameContainer
+            Histograms of sparse codes stored in the FrameContainer.
+        """
+
+        histograms = self.comp_hist_of_sparse_codes(frames, self.method)
+
+        frame_container = self.convert_sparse_codes_to_frame_container(histograms)
+
+        return frame_container
+
+
+    #==========================================================================
+    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.
+        """
+
+        self.video_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 = self.video_extractor.read_feature(file_name)
+
+        return frames

bob/pad/face/extractor/__init__.py

@@ -4,6 +4,7 @@ from .ImageQualityMeasure import ImageQualityMeasure
 from .VideoDataLoader import VideoDataLoader
 from .VideoQualityMeasure import VideoQualityMeasure
 from .FrameDiffFeatures import FrameDiffFeatures
+from .VideoHistOfSparseCodes import VideoHistOfSparseCodes
 
 def __appropriate__(*args):
     """Says object was actually declared here, and not in the import module.
@@ -30,5 +31,6 @@ __appropriate__(
     VideoQualityMeasure,
     VideoDataLoader,
     FrameDiffFeatures,
+    VideoHistOfSparseCodes,
 )
 __all__ = [_ for _ in dir() if not _.startswith('_')]

bob/pad/face/preprocessor/__init__.py

 from .VideoFaceCrop import VideoFaceCrop
 from .ImageFaceCrop import ImageFaceCrop
 from .FrameDifference import FrameDifference
+from .VideoSparseCoding import VideoSparseCoding
 
 
 def __appropriate__(*args):
@@ -25,5 +26,7 @@ __appropriate__(
     VideoFaceCrop,
     ImageFaceCrop,
     FrameDifference,
+    VideoSparseCoding,
 )
 __all__ = [_ for _ in dir() if not _.startswith('_')]
+

doc/index.rst

@@ -22,6 +22,7 @@ Users Guide
 
    installation
    baselines
+   other_pad_algorithms
    references
    resources
    api

doc/other_pad_algorithms.rst

+
+
+.. _bob.pad.face.other_pad_algorithms:
+
+
+===============================
+ Executing Other Algorithms
+===============================
+
+This section explains how to execute face presentation attack detection (PAD) algorithms implemented
+in ``bob.pad.face``.
+
+.. warning::
+
+   Algorithms introduced in this section might be in the process of publishing. Therefore, it is not
+   allowed to publish results introduced in this section without permission of the owner of the package.
+   If you are planning to use the results from this section, please contact the owner of the package first.
+   Please check the ``setup.py`` for contact information.
+
+
+Running face PAD Experiments
+------------------------------
+
+To run the PAD experiments, the ``spoof.py`` script located in ``bin`` directory is used.
+To see the description of the script you can type in the console:
+
+.. code-block:: sh
+
+   $ spoof.py --help
+
+This script is explained in more detail in :ref:`bob.pad.base.experiments`.
+
+Usually it is a good idea to have at least verbose level 2 (i.e., calling
+``spoof.py --verbose --verbose``, or the short version ``spoof.py
+-vv``).
+
+.. note:: **Running in Parallel**
+
+   To run the experiments in parallel, you can define an SGE grid or local host
+   (multi-processing) configurations as explained in
+   :ref:`running_in_parallel`.
+
+   In short, to run in the Idiap SGE grid, you can simply add the ``--grid``
+   command line option, with grid configuration parameters. To run experiments in parallel on
+   the local machine, simply add a ``--parallel <N>`` option, where ``<N>``
+   specifies the number of parallel jobs you want to execute.
+
+
+Database setups and face PAD algorithms are encoded using
+:ref:`bob.bio.base.configuration-files`, all stored inside the package root, in
+the directory ``bob/pad/face/config``. Documentation for each resource
+is available on the section :ref:`bob.pad.face.resources`.
+
+.. warning::
+
+   You **cannot** run experiments just by executing the command line
+   instructions described in this guide. You **need first** to procure yourself
+   the raw data files that correspond to *each* database used here in order to
+   correctly run experiments with those data. Biometric data is considered
+   private date and, under EU regulations, cannot be distributed without a
+   consent or license. You may consult our
+   :ref:`bob.pad.face.resources.databases` resources section for checking
+   currently supported databases and accessing download links for the raw data
+   files.
+
+   Once the raw data files have been downloaded, particular attention should be
+   given to the directory locations of those. Unpack the databases carefully
+   and annotate the root directory where they have been unpacked.
+
+   Then, carefully read the *Databases* section of
+   :ref:`bob.pad.base.installation` on how to correctly setup the
+   ``~/.bob_bio_databases.txt`` file.
+
+   Use the following keywords on the left side of the assignment (see
+   :ref:`bob.pad.face.resources.databases`):
+
+   .. code-block:: text
+
+      [YOUR_REPLAY_ATTACK_DIRECTORY] = /complete/path/to/replayattack-database/
+
+   Notice it is rather important to use the strings as described above,
+   otherwise ``bob.pad.base`` will not be able to correctly load your images.
+
+   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
+
+    ./bin/evaluate.py \
+    --dev-files \
+    <PATH_TO_STORE_THE_RESULTS_1>/grandtest/scores/scores-dev  \
+    <PATH_TO_STORE_THE_RESULTS_2>/grandtest/scores/scores-dev  \
+    <PATH_TO_STORE_THE_RESULTS_3>/grandtest/scores/scores-dev  \
+    <PATH_TO_STORE_THE_RESULTS_4>/grandtest/scores/scores-dev  \
+    --eval-files \
+    <PATH_TO_STORE_THE_RESULTS_1>/grandtest/scores/scores-eval \
+    <PATH_TO_STORE_THE_RESULTS_2>/grandtest/scores/scores-eval \
+    <PATH_TO_STORE_THE_RESULTS_3>/grandtest/scores/scores-eval \
+    <PATH_TO_STORE_THE_RESULTS_4>/grandtest/scores/scores-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" \
+    -F 7 \
+    --criterion EER \
+    --roc <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
+
+    ./bin/evaluate.py \
+    --dev-files \
+    <PATH_TO_STORE_THE_RESULTS_1>/photo-photo-video/scores/scores-dev  \
+    <PATH_TO_STORE_THE_RESULTS_2>/photo-photo-video/scores/scores-dev  \
+    <PATH_TO_STORE_THE_RESULTS_3>/photo-photo-video/scores/scores-dev  \
+    <PATH_TO_STORE_THE_RESULTS_4>/photo-photo-video/scores/scores-dev  \
+    --eval-files \
+    <PATH_TO_STORE_THE_RESULTS_1>/photo-photo-video/scores/scores-eval \
+    <PATH_TO_STORE_THE_RESULTS_2>/photo-photo-video/scores/scores-eval \
+    <PATH_TO_STORE_THE_RESULTS_3>/photo-photo-video/scores/scores-eval \
+    <PATH_TO_STORE_THE_RESULTS_4>/photo-photo-video/scores/scores-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" \
+    -F 7 \
+    --criterion EER \
+    --roc <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
+
+    ./bin/evaluate.py \
+    --dev-files \
+    <PATH_TO_STORE_THE_RESULTS_1>/video-video-photo/scores/scores-dev  \
+    <PATH_TO_STORE_THE_RESULTS_2>/video-video-photo/scores/scores-dev  \
+    <PATH_TO_STORE_THE_RESULTS_3>/video-video-photo/scores/scores-dev  \
+    <PATH_TO_STORE_THE_RESULTS_4>/video-video-photo/scores/scores-dev  \
+    --eval-files \
+    <PATH_TO_STORE_THE_RESULTS_1>/video-video-photo/scores/scores-eval \
+    <PATH_TO_STORE_THE_RESULTS_2>/video-video-photo/scores/scores-eval \
+    <PATH_TO_STORE_THE_RESULTS_3>/video-video-photo/scores/scores-eval \
+    <PATH_TO_STORE_THE_RESULTS_4>/video-video-photo/scores/scores-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" \
+    -F 7 \
+    --criterion EER \
+    --roc <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
+
+

doc/resources.rst

@@ -135,3 +135,32 @@ 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
+============================================================================================================================
+
+.. automodule:: bob.pad.face.config.qm_lr
+   :members:
+
+
+.. _bob.pad.face.resources.face_pad.qm_one_class_gmm:
+
+Image Quality Measures as features of facial region + GMM-based one-class classifier (anomaly detector)
+============================================================================================================================
+
+.. 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:
+
+

setup.py

@@ -103,13 +103,24 @@ setup(
         # registered preprocessors:
         'bob.pad.preprocessor': [
             'empty-preprocessor = bob.pad.face.config.preprocessor.filename:empty_preprocessor', # no preprocessing
+
+            # The sparse coding based preprocessors
+            'sparse-coding-preprocessor = bob.pad.face.config.preprocessor.video_sparse_coding:preprocessor',
+            'sparse-coding-preprocessor-10-5-16 = bob.pad.face.config.preprocessor.video_sparse_coding:preprocessor_10_5_16',
+            'sparse-coding-preprocessor-10-5-32 = bob.pad.face.config.preprocessor.video_sparse_coding:preprocessor_10_5_32',
+            'sparse-coding-preprocessor-10-5-64 = bob.pad.face.config.preprocessor.video_sparse_coding:preprocessor_10_5_64',
+            'sparse-coding-preprocessor-10-5-128 = bob.pad.face.config.preprocessor.video_sparse_coding:preprocessor_10_5_128',
             ],
 
-        # registered preprocessors:
+        # registered extractors:
         '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',
+
+            # extractors for sparse coding:
+            'hist-of-sparse-codes-mean = bob.pad.face.config.extractor.video_hist_of_sparse_codes:extractor_mean',
+            'hist-of-sparse-codes-hist = bob.pad.face.config.extractor.video_hist_of_sparse_codes:extractor_hist',
             ],
 
         # registered algorithms: