From 42800e57fa4089d04aa94441160ae22bc96bc153 Mon Sep 17 00:00:00 2001
From: Olegs NIKISINS <onikisins@italix03.idiap.ch>
Date: Mon, 2 Oct 2017 11:00:41 +0200
Subject: [PATCH] Updated the main doc on anomaly detection PAD for Aggregated
Db, some doc for VideoSparseCoding preprocessor
---
.../face/preprocessor/VideoSparseCoding.py | 29 ++
...qm_anomaly_detection_aggr_db_grandtest.pdf | Bin 0 -> 20029 bytes
...qm_anomaly_detection_aggr_db_ph_ph_vid.pdf | Bin 0 -> 20037 bytes
...m_anomaly_detection_aggr_db_vid_vid_ph.pdf | Bin 0 -> 19931 bytes
doc/index.rst | 1 +
doc/other_pad_algorithms.rst | 366 ++++++++++++++++++
doc/resources.rst | 29 ++
7 files changed, 425 insertions(+)
create mode 100644 doc/img/ROC_iqm_anomaly_detection_aggr_db_grandtest.pdf
create mode 100644 doc/img/ROC_iqm_anomaly_detection_aggr_db_ph_ph_vid.pdf
create mode 100644 doc/img/ROC_iqm_anomaly_detection_aggr_db_vid_vid_ph.pdf
create mode 100644 doc/other_pad_algorithms.rst
diff --git a/bob/pad/face/preprocessor/VideoSparseCoding.py b/bob/pad/face/preprocessor/VideoSparseCoding.py
index d4484adb..944ca82e 100644
--- a/bob/pad/face/preprocessor/VideoSparseCoding.py
+++ b/bob/pad/face/preprocessor/VideoSparseCoding.py
@@ -718,6 +718,35 @@ class VideoSparseCoding(Preprocessor, object):
def comp_hist_of_sparse_codes(self, frames, method):
"""
Compute the histograms of sparse codes.
+
+ **Parameters:**
+
+ ``frame_container`` : FrameContainer
+ FrameContainer containing the frames with sparse codes for the
+ frontal, horizontal and vertical patches. Each frame is a 3D array.
+ The dimensionality of array is:
+ (``3`` x ``n_samples`` x ``n_words_in_the_dictionary``).
+ First array [0,:,:] contains frontal sparse codes.
+ Second array [1,:,:] contains horizontal sparse codes.
+ Third array [2,:,:] contains vertical sparse codes.
+
+ ``method`` : :py:class:`str`
+ Name of the method to be used for combining the sparse codes into
+ a single feature vector. Two options are possible: "mean" and
+ "hist". If "mean" is selected the mean for ``n_samples`` dimension
+ is first computed. The resulting vectors for various types of
+ patches are then concatenated into a single feature vector.
+ If "hist" is selected, the values in the input array are first
+ binarized setting all non-zero elements to one. The rest of the
+ process is similar to the "mean" combination method.
+
+ **Returns:**
+
+ ``frame_container`` : FrameContainer
+ FrameContainer containing the frames with sparse codes for the
+ frontal, horizontal and vertical patches. Each frame is a 3D array.
+ The dimensionality of array is:
+ (``3`` x ``n_samples`` x ``n_words_in_the_dictionary``).
"""
histograms = []
diff --git a/doc/img/ROC_iqm_anomaly_detection_aggr_db_grandtest.pdf b/doc/img/ROC_iqm_anomaly_detection_aggr_db_grandtest.pdf
new file mode 100644
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diff --git a/doc/img/ROC_iqm_anomaly_detection_aggr_db_vid_vid_ph.pdf b/doc/img/ROC_iqm_anomaly_detection_aggr_db_vid_vid_ph.pdf
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literal 0
HcmV?d00001
diff --git a/doc/index.rst b/doc/index.rst
index fda461ea..f06c8df2 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -22,6 +22,7 @@ Users Guide
installation
baselines
+ other_pad_algorithms
references
resources
api
diff --git a/doc/other_pad_algorithms.rst b/doc/other_pad_algorithms.rst
new file mode 100644
index 00000000..ac7efc11
--- /dev/null
+++ b/doc/other_pad_algorithms.rst
@@ -0,0 +1,366 @@
+
+
+.. _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
+
+
diff --git a/doc/resources.rst b/doc/resources.rst
index ca93b11a..971e96de 100644
--- a/doc/resources.rst
+++ b/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:
+
+
--
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