diff --git a/bob/pad/face/config/lbp_svm.py b/bob/pad/face/config/lbp_svm.py
index 5ba874dc20f32f2e36b1322dcc507fbb84a3046c..3c95b9850cf7bb4745c6174fe5fdd2a92d25d371 100644
--- a/bob/pad/face/config/lbp_svm.py
+++ b/bob/pad/face/config/lbp_svm.py
@@ -5,13 +5,8 @@
@author: Olegs Nikisins
This file contains configurations to run LBP and SVM based face PAD baseline.
-
The settings are tuned for the Replay-attack database.
-
-The idea of the algorithm is introduced in the following paper:
-
-[CAM12]_
-
+The idea of the algorithm is introduced in the following paper: [CAM12]_.
However some settings are different from the ones introduced in the paper.
"""
diff --git a/bob/pad/face/config/qm_svm.py b/bob/pad/face/config/qm_svm.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcf1473c76f85b3ea806324e55d4f60152c6e95c
--- /dev/null
+++ b/bob/pad/face/config/qm_svm.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+
+"""
+@author: Olegs Nikisins
+
+This file contains configurations to run Image Quality Measures (IQM) and SVM based face PAD baseline.
+The settings are tuned for the Replay-attack database.
+The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
+"""
+
+
+#=======================================================================================
+sub_directory = 'qm_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 VideoFaceCrop
+
+CROPPED_IMAGE_SIZE = (64, 64) # The size of the resulting face
+CROPPED_POSITIONS = {'topleft' : (0,0) , 'bottomright' : CROPPED_IMAGE_SIZE}
+FIXED_POSITIONS = None
+MASK_SIGMA = None # The sigma for random values areas outside image
+MASK_NEIGHBORS = 5 # The number of neighbors to consider while extrapolating
+MASK_SEED = None # The seed for generating random values during extrapolation
+CHECK_FACE_SIZE_FLAG = True # Check the size of the face
+MIN_FACE_SIZE = 50
+USE_LOCAL_CROPPER_FLAG = True # Use the local face cropping class (identical to Ivana's paper)
+RGB_OUTPUT_FLAG = True # Return RGB cropped face using local cropper
+
+preprocessor = VideoFaceCrop(cropped_image_size = CROPPED_IMAGE_SIZE,
+ cropped_positions = CROPPED_POSITIONS,
+ fixed_positions = FIXED_POSITIONS,
+ mask_sigma = MASK_SIGMA,
+ mask_neighbors = MASK_NEIGHBORS,
+ mask_seed = None,
+ check_face_size_flag = CHECK_FACE_SIZE_FLAG,
+ min_face_size = MIN_FACE_SIZE,
+ use_local_cropper_flag = USE_LOCAL_CROPPER_FLAG,
+ rgb_output_flag = RGB_OUTPUT_FLAG)
+"""
+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 ``cropped_image_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 ``use_local_cropper_flag = True``. The preprocessed frame is the RGB
+facial image, which is defined by ``RGB_OUTPUT_FLAG = True``.
+"""
+
+
+#=======================================================================================
+# define extractor:
+
+from ..extractor import VideoQualityMeasure
+
+GALBALLY=True
+MSU=True
+DTYPE=None
+
+extractor = VideoQualityMeasure(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 ..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/doc/baselines.rst b/doc/baselines.rst
index 473af1faf81816ae02d7e36fe635129da3149090..7569495f0bd047988573b14191e8ef71ef49a4d2 100644
--- a/doc/baselines.rst
+++ b/doc/baselines.rst
@@ -93,7 +93,7 @@ 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`.
-To run this baseline on the `replayattack`_ database, using the ``grandtest`` protocol, do the following:
+To run this baseline on the `replayattack`_ database, using the ``grandtest`` protocol, execute the following:
.. code-block:: sh
@@ -148,18 +148,49 @@ The ROC curves for the particular experiment can be downloaded from here:
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`.
+To run this baseline on the `replayattack`_ database, using the ``grandtest`` protocol, execute the following:
+.. code-block:: sh
+ $ ./bin/spoof.py qm-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/qm_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 "IQM features of facial region + 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`` | 4.321 | 4.570 |
++-------------------+----------+----------+
+
+The ROC curves for the particular experiment can be downloaded from here:
+:download:`ROC curve <img/ROC_iqm_svm_replay_attack.pdf>`
+
+------------
.. include:: links.rst
diff --git a/doc/img/ROC_iqm_svm_replay_attack.pdf b/doc/img/ROC_iqm_svm_replay_attack.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..a4524065a5d3f1e41e547ae51f63859db6c505d9
Binary files /dev/null and b/doc/img/ROC_iqm_svm_replay_attack.pdf differ
diff --git a/doc/img/ROC_lbp_svm_replay_attack.pdf b/doc/img/ROC_lbp_svm_replay_attack.pdf
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..700f2f2ff4f617b49ba18a9cb52863c679caaa82 100644
Binary files a/doc/img/ROC_lbp_svm_replay_attack.pdf and b/doc/img/ROC_lbp_svm_replay_attack.pdf differ
diff --git a/doc/references.rst b/doc/references.rst
index 8120f84e9aeb234fa5fc8fcf0813065cc4b135fb..911101849170b2dd52a7cd6d1a885887bf7b9166 100644
--- a/doc/references.rst
+++ b/doc/references.rst
@@ -7,3 +7,8 @@ References
.. [CAM12] *I. Chingovska, A. Anjos, and S. Marcel*, **On the effectiveness of local binary patterns in face anti-spoofing**,
in: Biometrics Special Interest Group (BIOSIG), 2012 BIOSIG - Proceedings of the International Conference of the, 2012, pp. 1-7.
+.. [WHJ15] *Di Wen, Member, IEEE, Hu Han, Member, IEEE and Anil K. Jain, Fellow, IEEE*, **Face Spoof Detection with Image Distortion Analysis**,
+ in: IEEE Transactions on Information Forensics and Security, 2015.
+
+.. [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.
diff --git a/doc/resources.rst b/doc/resources.rst
index c5814500208bc404fa90a25e4db83973a87a7d34..7b9d574bc3e99c013c3cac8dc84f8d436745dbec 100644
--- a/doc/resources.rst
+++ b/doc/resources.rst
@@ -45,4 +45,13 @@ LBP features of facial region + SVM for REPLAY-ATTACK
======================================================
.. automodule:: bob.pad.face.config.lbp_svm
+ :members:
+
+
+.. _bob.pad.face.resources.face_pad.qm_svm_replayattack:
+
+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
diff --git a/setup.py b/setup.py
index 7a2d5e34d244bdbbc8abe7b29779735e7f14c389..116ef87a3ecd4afa947dfcf904ee833949483343 100644
--- a/setup.py
+++ b/setup.py
@@ -103,6 +103,7 @@ setup(
# baselines:
'lbp-svm = bob.pad.face.config.lbp_svm',
+ 'qm-svm = bob.pad.face.config.qm_svm',
],
# registered preprocessors: