diff --git a/bob/pad/face/config/lbp_svm_aggregated_db.py b/bob/pad/face/config/lbp_svm_aggregated_db.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d249a7c83ed3169319e06130096f89a1e725ca7
--- /dev/null
+++ b/bob/pad/face/config/lbp_svm_aggregated_db.py
@@ -0,0 +1,116 @@
+#!/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 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 # Minimal possible size of the face
+USE_LOCAL_CROPPER_FLAG = True # Use the local face cropping class (identical to Ivana's paper)
+COLOR_CHANNEL = 'gray' # Convert image to gray-scale format
+
+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,
+ color_channel = COLOR_CHANNEL)
+"""
+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 with 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``.
+"""
+
+
+#=======================================================================================
+# define extractor:
+
+from ..extractor import VideoLBPHistogram
+
+LBPTYPE='uniform'
+ELBPTYPE='regular'
+RAD=1
+NEIGHBORS=8
+CIRC=False
+DTYPE=None
+
+extractor = VideoLBPHistogram(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 ..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
+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 = 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,
+ 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``.
+"""
\ No newline at end of file
diff --git a/doc/resources.rst b/doc/resources.rst
index 460737e0646d04fc6aaea59254e28843934651d4..2407852e7ffe3777d74e71c60e146f8fef74736d 100644
--- a/doc/resources.rst
+++ b/doc/resources.rst
@@ -100,6 +100,15 @@ 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
@@ -109,8 +118,13 @@ Image Quality Measures as features of facial region + SVM for Aggregated Databas
: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:
diff --git a/setup.py b/setup.py
index ce17ee729f1523d524b25f7d57695b1d65fc5f12..e0ac7ee6cbf6fbe56e0b6a9754bded38486ae3db 100644
--- a/setup.py
+++ b/setup.py
@@ -74,6 +74,7 @@ setup(
# baselines:
'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',