diff --git a/bob/bio/face/config/baseline/helpers.py b/bob/bio/face/config/baseline/helpers.py
index eda47399581d7cd723bf5e98abe311bfccb11dfa..7bb2b74b414555e43ac312a30888d1bd5cc3fca2 100644
--- a/bob/bio/face/config/baseline/helpers.py
+++ b/bob/bio/face/config/baseline/helpers.py
@@ -5,6 +5,7 @@ from bob.pipelines import wrap
from bob.bio.face.helpers import face_crop_solver
import numpy as np
+
def embedding_transformer_default_cropping(cropped_image_size, annotation_type):
"""
Computes the default cropped positions for the FaceCropper used with Facenet-like
@@ -32,32 +33,51 @@ def embedding_transformer_default_cropping(cropped_image_size, annotation_type):
TOP_LEFT_POS = (0, 0)
BOTTOM_RIGHT_POS = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
- cropped_positions={"topleft": TOP_LEFT_POS, "bottomright": BOTTOM_RIGHT_POS}
+ cropped_positions = {"topleft": TOP_LEFT_POS, "bottomright": BOTTOM_RIGHT_POS}
elif annotation_type == "eyes-center":
- RIGHT_EYE_POS = (round(2/7*CROPPED_IMAGE_HEIGHT), round(1/3*CROPPED_IMAGE_WIDTH))
- LEFT_EYE_POS = (round(2/7*CROPPED_IMAGE_HEIGHT), round(2/3*CROPPED_IMAGE_WIDTH))
- cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS}
+ RIGHT_EYE_POS = (
+ round(2 / 7 * CROPPED_IMAGE_HEIGHT),
+ round(1 / 3 * CROPPED_IMAGE_WIDTH),
+ )
+ LEFT_EYE_POS = (
+ round(2 / 7 * CROPPED_IMAGE_HEIGHT),
+ round(2 / 3 * CROPPED_IMAGE_WIDTH),
+ )
+ cropped_positions = {"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS}
elif annotation_type == "left-profile":
- EYE_POS = (round(2/7*CROPPED_IMAGE_HEIGHT), round(3/8*CROPPED_IMAGE_WIDTH))
- MOUTH_POS = (round(5/7*CROPPED_IMAGE_HEIGHT), round(3/8*CROPPED_IMAGE_WIDTH))
- cropped_positions={'leye': EYE_POS, 'mouth': MOUTH_POS}
+ EYE_POS = (
+ round(2 / 7 * CROPPED_IMAGE_HEIGHT),
+ round(3 / 8 * CROPPED_IMAGE_WIDTH),
+ )
+ MOUTH_POS = (
+ round(5 / 7 * CROPPED_IMAGE_HEIGHT),
+ round(3 / 8 * CROPPED_IMAGE_WIDTH),
+ )
+ cropped_positions = {"leye": EYE_POS, "mouth": MOUTH_POS}
elif annotation_type == "right-profile":
- EYE_POS = (round(2/7*CROPPED_IMAGE_HEIGHT), round(5/8*CROPPED_IMAGE_WIDTH))
- MOUTH_POS = (round(5/7*CROPPED_IMAGE_HEIGHT), round(5/8*CROPPED_IMAGE_WIDTH))
- cropped_positions={'reye': EYE_POS, 'mouth': MOUTH_POS}
-
+ EYE_POS = (
+ round(2 / 7 * CROPPED_IMAGE_HEIGHT),
+ round(5 / 8 * CROPPED_IMAGE_WIDTH),
+ )
+ MOUTH_POS = (
+ round(5 / 7 * CROPPED_IMAGE_HEIGHT),
+ round(5 / 8 * CROPPED_IMAGE_WIDTH),
+ )
+ cropped_positions = {"reye": EYE_POS, "mouth": MOUTH_POS}
+
else:
cropped_positions = None
return cropped_positions
+
def legacy_default_cropping(cropped_image_size, annotation_type):
"""
Computes the default cropped positions for the FaceCropper used with legacy extractors,
@@ -85,33 +105,41 @@ def legacy_default_cropping(cropped_image_size, annotation_type):
TOP_LEFT_POS = (0, 0)
BOTTOM_RIGHT_POS = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
- cropped_positions={"topleft": TOP_LEFT_POS, "bottomright": BOTTOM_RIGHT_POS}
+ cropped_positions = {"topleft": TOP_LEFT_POS, "bottomright": BOTTOM_RIGHT_POS}
elif annotation_type == "eyes-center":
RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
- cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS}
+ cropped_positions = {"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS}
elif annotation_type == "left-profile":
# Main reference https://gitlab.idiap.ch/bob/bob.chapter.FRICE/-/blob/master/bob/chapter/FRICE/script/pose.py
- EYE_POS = (CROPPED_IMAGE_HEIGHT//5, CROPPED_IMAGE_WIDTH // 7 * 3 - 2)
- MOUTH_POS = (CROPPED_IMAGE_HEIGHT//3 * 2, CROPPED_IMAGE_WIDTH // 7 * 3 - 2)
- cropped_positions={'leye': EYE_POS, 'mouth': MOUTH_POS}
+ EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 7 * 3 - 2)
+ MOUTH_POS = (CROPPED_IMAGE_HEIGHT // 3 * 2, CROPPED_IMAGE_WIDTH // 7 * 3 - 2)
+ cropped_positions = {"leye": EYE_POS, "mouth": MOUTH_POS}
elif annotation_type == "right-profile":
# Main reference https://gitlab.idiap.ch/bob/bob.chapter.FRICE/-/blob/master/bob/chapter/FRICE/script/pose.py
- EYE_POS = (CROPPED_IMAGE_HEIGHT//5, CROPPED_IMAGE_WIDTH // 7 * 4 + 2)
- MOUTH_POS = (CROPPED_IMAGE_HEIGHT//3 * 2, CROPPED_IMAGE_WIDTH // 7 * 4 + 2)
- cropped_positions={'reye': EYE_POS, 'mouth': MOUTH_POS}
-
+ EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 7 * 4 + 2)
+ MOUTH_POS = (CROPPED_IMAGE_HEIGHT // 3 * 2, CROPPED_IMAGE_WIDTH // 7 * 4 + 2)
+ cropped_positions = {"reye": EYE_POS, "mouth": MOUTH_POS}
+
else:
cropped_positions = None
return cropped_positions
-def embedding_transformer(cropped_image_size, embedding, annotation_type, cropped_positions, fixed_positions=None, color_channel = "rgb"):
+
+def embedding_transformer(
+ cropped_image_size,
+ embedding,
+ annotation_type,
+ cropped_positions,
+ fixed_positions=None,
+ color_channel="rgb",
+):
"""
Creates a pipeline composed by and FaceCropper and an Embedding extractor.
This transformer is suited for Facenet based architectures
@@ -121,13 +149,15 @@ def embedding_transformer(cropped_image_size, embedding, annotation_type, croppe
"""
face_cropper = face_crop_solver(
- cropped_image_size,
- color_channel=color_channel,
- cropped_positions=cropped_positions,
- fixed_positions=fixed_positions,
- )
+ cropped_image_size,
+ color_channel=color_channel,
+ cropped_positions=cropped_positions,
+ fixed_positions=fixed_positions,
+ )
- transform_extra_arguments = None if cropped_positions is None else (("annotations", "annotations"),)
+ transform_extra_arguments = (
+ None if cropped_positions is None else (("annotations", "annotations"),)
+ )
transformer = make_pipeline(
wrap(
@@ -140,7 +170,10 @@ def embedding_transformer(cropped_image_size, embedding, annotation_type, croppe
return transformer
-def embedding_transformer_160x160(embedding, annotation_type, fixed_positions, color_channel="rgb"):
+
+def embedding_transformer_160x160(
+ embedding, annotation_type, fixed_positions, color_channel="rgb"
+):
"""
Creates a pipeline composed by and FaceCropper and an Embedding extractor.
This transformer is suited for Facenet based architectures
@@ -149,12 +182,23 @@ def embedding_transformer_160x160(embedding, annotation_type, fixed_positions, c
This will resize images to :math:`160 \times 160`
"""
- cropped_positions = embedding_transformer_default_cropping((160, 160), annotation_type)
+ cropped_positions = embedding_transformer_default_cropping(
+ (160, 160), annotation_type
+ )
- return embedding_transformer((160, 160), embedding, annotation_type, cropped_positions, fixed_positions, color_channel=color_channel)
+ return embedding_transformer(
+ (160, 160),
+ embedding,
+ annotation_type,
+ cropped_positions,
+ fixed_positions,
+ color_channel=color_channel,
+ )
-def embedding_transformer_112x112(embedding, annotation_type, fixed_positions, color_channel="rgb"):
+def embedding_transformer_112x112(
+ embedding, annotation_type, fixed_positions, color_channel="rgb"
+):
"""
Creates a pipeline composed by and FaceCropper and an Embedding extractor.
This transformer is suited for Facenet based architectures
@@ -166,12 +210,52 @@ def embedding_transformer_112x112(embedding, annotation_type, fixed_positions, c
cropped_image_size = (112, 112)
if annotation_type == "eyes-center":
# Hard coding eye positions for backward consistency
- cropped_positions = {'leye': (32, 77), 'reye': (32, 34)}
+ cropped_positions = {"leye": (32, 77), "reye": (32, 34)}
else:
- # Will use default
- cropped_positions = embedding_transformer_default_cropping(cropped_image_size, annotation_type)
+ # Will use default
+ cropped_positions = embedding_transformer_default_cropping(
+ cropped_image_size, annotation_type
+ )
- return embedding_transformer(cropped_image_size, embedding, annotation_type, cropped_positions, fixed_positions, color_channel=color_channel)
+ return embedding_transformer(
+ cropped_image_size,
+ embedding,
+ annotation_type,
+ cropped_positions,
+ fixed_positions,
+ color_channel=color_channel,
+ )
+
+
+def embedding_transformer_224x224(
+ embedding, annotation_type, fixed_positions, color_channel="rgb"
+):
+ """
+ Creates a pipeline composed by and FaceCropper and an Embedding extractor.
+ This transformer is suited for Facenet based architectures
+
+ .. warning::
+ This will resize images to :math:`112 \times 112`
+
+ """
+ cropped_image_size = (224, 224)
+ if annotation_type == "eyes-center":
+ # Hard coding eye positions for backward consistency
+ cropped_positions = {"leye": (65, 150), "reye": (65, 77)}
+ else:
+ # Will use default
+ cropped_positions = embedding_transformer_default_cropping(
+ cropped_image_size, annotation_type
+ )
+
+ return embedding_transformer(
+ cropped_image_size,
+ embedding,
+ annotation_type,
+ cropped_positions,
+ fixed_positions,
+ color_channel=color_channel,
+ )
def crop_80x64(annotation_type, fixed_positions=None, color_channel="gray"):
@@ -212,15 +296,16 @@ def crop_80x64(annotation_type, fixed_positions=None, color_channel="gray"):
cropped_positions = legacy_default_cropping(cropped_image_size, annotation_type)
-
face_cropper = face_crop_solver(
- cropped_image_size,
- color_channel=color_channel,
- cropped_positions=cropped_positions,
- fixed_positions=fixed_positions,
- dtype=dtype
- )
-
- transform_extra_arguments = None if cropped_positions is None else (("annotations", "annotations"),)
+ cropped_image_size,
+ color_channel=color_channel,
+ cropped_positions=cropped_positions,
+ fixed_positions=fixed_positions,
+ dtype=dtype,
+ )
+
+ transform_extra_arguments = (
+ None if cropped_positions is None else (("annotations", "annotations"),)
+ )
return face_cropper, transform_extra_arguments
diff --git a/bob/bio/face/preprocessor/FaceCrop.py b/bob/bio/face/preprocessor/FaceCrop.py
index 9b04b9decc79e3099b987b1e67c2843e34a096e0..ca9a77c46ac73242f615819a8dbba68e5805983a 100644
--- a/bob/bio/face/preprocessor/FaceCrop.py
+++ b/bob/bio/face/preprocessor/FaceCrop.py
@@ -93,7 +93,7 @@ class FaceCrop(Base):
your database easily. If you are sure about your input, you can set this flag to
``True``.
- annotator : :any:`bob.bio.base.annotator.Annotator`
+ annotator : `bob.bio.base.annotator.Annotator`
If provided, the annotator will be used if the required annotations are
missing.
diff --git a/bob/bio/face/preprocessor/HistogramEqualization.py b/bob/bio/face/preprocessor/HistogramEqualization.py
index 63f7a594bd06b99cf4eb51be8bf396310aebbefa..95523e97bc39f29bfc584756bc7325c3d6e83686 100644
--- a/bob/bio/face/preprocessor/HistogramEqualization.py
+++ b/bob/bio/face/preprocessor/HistogramEqualization.py
@@ -28,8 +28,8 @@ from bob.pipelines.sample import SampleBatch
class HistogramEqualization(Base):
"""Crops the face (if desired) and performs histogram equalization to photometrically enhance the image.
- Parameters:
- -----------
+ Parameters
+ ----------
face_cropper : str or :py:class:`bob.bio.face.preprocessor.FaceCrop` or :py:class:`bob.bio.face.preprocessor.FaceDetect` or ``None``
The face image cropper that should be applied to the image.
diff --git a/doc/annotators.rst b/doc/annotators.rst
index b7cb0abf8051202fed2ee56924bb6df2863e5050..2277b00fa989e4591d4087dfa6d556b1ab159ed7 100644
--- a/doc/annotators.rst
+++ b/doc/annotators.rst
@@ -7,7 +7,7 @@
=================
This packages provides several face annotators (using RGB images) that you can
-use to annotate biometric databases. See :ref:`bob.bio.base.annotations` for
+use to annotate biometric databases. See :any:`bob.bio.base.annotator` for
a guide on the general usage of this feature.
.. warning::
diff --git a/doc/baselines.rst b/doc/baselines.rst
index 8ce69a0c0f8d976465660972895bb518360b61a2..2217e5af8bf96bb90dc7b6964dee14f5fbdca275 100644
--- a/doc/baselines.rst
+++ b/doc/baselines.rst
@@ -8,60 +8,51 @@
Executing Baseline Algorithms
=============================
-.. todo::
- Here we should:
- - Brief how to run an experiment
- - Point to bob.bio.base for further explanation
- - Show the baselines available
- - Show the databases available
+In this section we introduce the baselines available in this pakcage.
+To execute one of then in the databases available just run the following command::
-The baselines
--------------
+$ bob bio pipelines vanilla-biometrics [DATABASE_NAME] [BASELINE]
-The algorithms present an (incomplete) set of state-of-the-art face recognition algorithms. Here is the list of short-cuts:
+.. note::
+ Both, `[DATABASE_NAME]` and `[BASELINE]` can be either python resources or
+ python files.
-* ``eigenface``: The eigenface algorithm as proposed by [TP91]_. It uses the pixels as raw data, and applies a *Principal Component Analysis* (PCA) on it:
+ Please, refer to :ref:`bob.bio.base <bob.bio.base>` for more information.
- - preprocessor : :py:class:`bob.bio.face.preprocessor.FaceCrop`
- - feature : :py:class:`bob.bio.base.extractor.Linearize`
- - algorithm : :py:class:`bob.bio.base.algorithm.PCA`
-* ``lda``: The LDA algorithm applies a *Linear Discriminant Analysis* (LDA), here we use the combined PCA+LDA approach [ZKC98]_:
- - preprocessor : :py:class:`bob.bio.face.preprocessor.FaceCrop`
- - feature : :py:class:`bob.bio.face.extractor.Eigenface`
- - algorithm : :py:class:`bob.bio.base.algorithm.LDA`
+Baselines available
+-------------------
-* ``gaborgraph``: This method extract grid graphs of Gabor jets from the images, and computes a Gabor phase based similarity [GHW12]_.
+The algorithms below constains all the face recognition baselines available.
+It is split in two groups, before and after deep learning era.
- - preprocessor : :py:class:`bob.bio.face.preprocessor.INormLBP`
- - feature : :py:class:`bob.bio.face.extractor.GridGraph`
- - algorithm : :py:class:`bob.bio.face.algorithm.GaborJet`
+Before Deep learning era
+========================
-Further algorithms are available, when the :ref:`bob.bio.gmm <bob.bio.gmm>` package is installed:
-* ``gmm``: *Gaussian Mixture Models* (GMM) [MM09]_ are extracted from *Discrete Cosine Transform* (DCT) block features.
+* ``eigenface``: The eigenface algorithm as proposed by [TP91]_. It uses the pixels as raw data, and applies a *Principal Component Analysis* (PCA) on it.
- - preprocessor : :py:class:`bob.bio.face.preprocessor.TanTriggs`
- - feature : :py:class:`bob.bio.face.extractor.DCTBlocks`
- - algorithm : :py:class:`bob.bio.gmm.algorithm.GMM`
+* ``lda``: The LDA algorithm applies a *Linear Discriminant Analysis* (LDA), here we use the combined PCA+LDA approach [ZKC98]_
-* ``isv``: As an extension of the GMM algorithm, *Inter-Session Variability* (ISV) modeling [WMM11]_ is used to learn what variations in images are introduced by identity changes and which not.
+* ``gabor_graph``: This method extract grid graphs of Gabor jets from the images, and computes a Gabor phase based similarity [GHW12]_.
- - preprocessor : :py:class:`bob.bio.face.preprocessor.TanTriggs`
- - feature : :py:class:`bob.bio.face.extractor.DCTBlocks`
- - algorithm : :py:class:`bob.bio.gmm.algorithm.ISV`
+* ``lgbphs``: Local Gabor binary pattern histogram sequence (LGBPHS) implemented in [ZSG05]_
-* ``ivector``: Another extension of the GMM algorithm is *Total Variability* (TV) modeling [WM12]_ (aka. I-Vector), which tries to learn a subspace in the GMM super-vector space.
- - preprocessor : :py:class:`bob.bio.face.preprocessor.TanTriggs`
- - feature : :py:class:`bob.bio.face.extractor.DCTBlocks`
- - algorithm : :py:class:`bob.bio.gmm.algorithm.IVector`
+Deep learning baselines
+=======================
-.. note::
- The ``ivector`` algorithm needs a lot of training data and fails on small databases such as the `AT&T database`_.
+* ``facenet-sanderberg``: FaceNet trained by `David Sanderberg <https://github.com/davidsandberg/facenet>`_
+
+* ``inception-resnetv2-msceleb``: Inception Resnet v2 model trained using the MSCeleb dataset in the context of the work published by [TFP18]_
+
+* ``inception-resnetv1-msceleb``: Inception Resnet v1 model trained using the MSCeleb dataset in the context of the work published by [TFP18]_
+
+* ``inception-resnetv2-casiawebface``: Inception Resnet v2 model trained using the Casia Web dataset in the context of the work published by [TFP18]_
-.. _bob.bio.base.baseline_results:
+* ``inception-resnetv1-casiawebface``: Inception Resnet v1 model trained using the Casia Web dataset in the context of the work published by [TFP18]_
+* ``arcface-insightface``: Arcface model from `Insightface <https://github.com/deepinsight/insightface>`_
diff --git a/doc/implemented.rst b/doc/implemented.rst
index 19ab5aaa11b52d36d506f9de79b9af0cdc393ccc..ee0af21d26b1c13033a9d5d395a9a3dca6fc6f80 100644
--- a/doc/implemented.rst
+++ b/doc/implemented.rst
@@ -14,15 +14,11 @@ Databases
bob.bio.face.database.ARFaceBioDatabase
bob.bio.face.database.AtntBioDatabase
bob.bio.face.database.MobioBioDatabase
- bob.bio.face.database.CaspealBioDatabase
bob.bio.face.database.ReplayBioDatabase
bob.bio.face.database.ReplayMobileBioDatabase
bob.bio.face.database.GBUBioDatabase
bob.bio.face.database.LFWBioDatabase
bob.bio.face.database.MultipieBioDatabase
- bob.bio.face.database.XM2VTSBioDatabase
- bob.bio.face.database.FRGCBioDatabase
- bob.bio.face.database.SCFaceBioDatabase
bob.bio.face.database.FargoBioDatabase
diff --git a/doc/index.rst b/doc/index.rst
index 23bae2667d36221480784e589799554622cb7168..e0fbecd27ea700dc708cb89bb58339ec9312e274 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -4,9 +4,9 @@
.. _bob.bio.face:
-=============================
- Open Source Face Recognition
-=============================
+=====================================
+ Open Source Face Recognition Library
+=====================================
This package provide open source tools to run comparable and reproducible face recognition experiments.
@@ -15,32 +15,30 @@ This includes:
* Preprocessors to detect, align and photometrically enhance face images
* Feature extractors that extract features from facial images
* Facial image databases including their protocols.
-* Scripts that trains CNNs
-
-For more detailed information about how this package is structured, please refer to the documentation of :ref:`bob.bio.base <bob.bio.base>`.
+* Scripts that trains CNNs for face recognition.
Get Started
-============
+===========
The easiest way to get started is by simply comparing two faces::
-$ bob bio compare-samples -p gabor_graph me.png not_me.png
+$ bob bio compare-samples -p facenet-sanderberg me.png not_me.png
.. warning::
No face detection is carried out with this command.
Check out all the face recognition algorithms available by doing::
-$ resources.py --type p
+$ resources.py --types p
Get Started, serious
====================
-.. todo::
-
- Briefing about baselines
+For detailed information on how this package is structured and how
+to run experiments with it, please refer to the documentation of :ref:`bob.bio.base <bob.bio.base>`
+and get to know the vanilla biometrics and how to integrate both, algorithm and database protocols with it.
Users Guide
@@ -50,7 +48,7 @@ Users Guide
:maxdepth: 2
baselines
- leaderboad
+ leaderboard/leaderboard
references
annotators
diff --git a/doc/leaderboard.rst b/doc/leaderboard.rst
deleted file mode 100644
index 08c1257eff684a8c215099a5b9f39c3cbf734d9b..0000000000000000000000000000000000000000
--- a/doc/leaderboard.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-.. vim: set fileencoding=utf-8 :
-
-.. _bob.bio.face.learderboard:
-
-=============================
-Leaderboad
-=============================
-
-.. todo::
- Here we should:
- - Present a custom Leaderboad per database
- - Present a script that runs at least one experiment of this leader board
-
-
diff --git a/doc/leaderboard/arface.rst b/doc/leaderboard/arface.rst
new file mode 100644
index 0000000000000000000000000000000000000000..fadb1e0ba313fe2f5c3fcd95a25a9b66f427f61f
--- /dev/null
+++ b/doc/leaderboard/arface.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.arface:
+
+==============
+ARFACE Dataset
+==============
+
+
+.. todo::
+ Present benchmarks
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/banca.rst b/doc/leaderboard/banca.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3a467606d1d0df1b20b72b0576a704461461426d
--- /dev/null
+++ b/doc/leaderboard/banca.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.banca:
+
+=============
+Banca Dataset
+=============
+
+
+.. todo::
+ Present benchmarks
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/gbu.rst b/doc/leaderboard/gbu.rst
new file mode 100644
index 0000000000000000000000000000000000000000..70fbe8fb8f215c12808ee72306a667b10c96a1f6
--- /dev/null
+++ b/doc/leaderboard/gbu.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.gbu:
+
+===========
+GBU Dataset
+===========
+
+
+.. todo::
+ Present benchmarks
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/ijbc.rst b/doc/leaderboard/ijbc.rst
new file mode 100644
index 0000000000000000000000000000000000000000..67d409ec417bc3fb62fdddaab1ad974b82ed7d70
--- /dev/null
+++ b/doc/leaderboard/ijbc.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.ijbc:
+
+=============
+IJB-C Dataset
+=============
+
+
+.. todo::
+ Present benchmarks
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/leaderboard.rst b/doc/leaderboard/leaderboard.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e86d2c0d5ea9d58e89799f94bf024b8a89639d54
--- /dev/null
+++ b/doc/leaderboard/leaderboard.rst
@@ -0,0 +1,28 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard:
+
+==========
+Leaderboad
+==========
+
+In the following pages we present a face recognition learderboard with some popular datasets.
+
+Datasets
+--------
+
+.. toctree::
+ :maxdepth: 2
+
+ mobio
+ lfw
+ meds
+ morph
+ ijbc
+ uccs
+ multipie
+ arface
+ xm2vts
+ gbu
+ banca
+
diff --git a/doc/leaderboard/lfw.rst b/doc/leaderboard/lfw.rst
new file mode 100644
index 0000000000000000000000000000000000000000..4fda5d6f07dc004d9b783da7a916f24ee007b633
--- /dev/null
+++ b/doc/leaderboard/lfw.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.lfw:
+
+===========
+LFW Dataset
+===========
+
+
+.. todo::
+ Present benchmarks
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/meds.rst b/doc/leaderboard/meds.rst
new file mode 100644
index 0000000000000000000000000000000000000000..934a13b1fe957d64c2ffaf513a8b8a8ed0ae9695
--- /dev/null
+++ b/doc/leaderboard/meds.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.meds:
+
+============
+MEDS Dataset
+============
+
+
+.. todo::
+ Present benchmarks
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/mobio.rst b/doc/leaderboard/mobio.rst
new file mode 100644
index 0000000000000000000000000000000000000000..708566505b5b666b590f415f0b2f2545b25048b4
--- /dev/null
+++ b/doc/leaderboard/mobio.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.mobio:
+
+=============
+Mobio Dataset
+=============
+
+
+.. todo::
+ Benchmarks on Mobio Database
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/morph.rst b/doc/leaderboard/morph.rst
new file mode 100644
index 0000000000000000000000000000000000000000..1bfc35311f19ec4f77bcf5a085abe3fd36480fac
--- /dev/null
+++ b/doc/leaderboard/morph.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.morph:
+
+=============
+Morph Dataset
+=============
+
+
+.. todo::
+ Present benchmarks
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/multipie.rst b/doc/leaderboard/multipie.rst
new file mode 100644
index 0000000000000000000000000000000000000000..035a819009221259de2e76bc1744a4998d047ce8
--- /dev/null
+++ b/doc/leaderboard/multipie.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.multipie:
+
+================
+Multipie Dataset
+================
+
+
+.. todo::
+ Benchmarks on Multipie Database
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/uccs.rst b/doc/leaderboard/uccs.rst
new file mode 100644
index 0000000000000000000000000000000000000000..003d37380986284247405b8cb197c64b2578aa88
--- /dev/null
+++ b/doc/leaderboard/uccs.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.uccs:
+
+============
+UCCS Dataset
+============
+
+
+.. todo::
+ Present benchmarks
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/leaderboard/xm2vts.rst b/doc/leaderboard/xm2vts.rst
new file mode 100644
index 0000000000000000000000000000000000000000..faf38692442f086ad0a728c411172ba3e6e1493f
--- /dev/null
+++ b/doc/leaderboard/xm2vts.rst
@@ -0,0 +1,13 @@
+.. vim: set fileencoding=utf-8 :
+
+.. _bob.bio.face.learderboard.xm2vts:
+
+==============
+XM2VTS Dataset
+==============
+
+
+.. todo::
+ Present benchmarks
+
+ Probably for Manuel's students
\ No newline at end of file
diff --git a/doc/references.rst b/doc/references.rst
index 22fb8ffc61e6e09e7706923be6d9501f3eb254d5..ea60e1ad5e565618d66c19fe260dabb1b4b4df45 100644
--- a/doc/references.rst
+++ b/doc/references.rst
@@ -8,19 +8,12 @@ References
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.. [ZKC98] *W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets and J. Weng*. **Discriminant analysis of principal components for face recognition**, pages 73-85. Springer Verlag Berlin, 1998.
-.. [MWP98] *B. Moghaddam, W. Wahid and A. Pentland*. **Beyond eigenfaces: probabilistic matching for face recognition**. IEEE International Conference on Automatic Face and Gesture Recognition, pages 30-35. 1998.
.. [GHW12] *M. Günther, D. Haufe and R.P. Würtz*. **Face recognition with disparity corrected Gabor phase differences**. In Artificial neural networks and machine learning, volume 7552 of Lecture Notes in Computer Science, pages 411-418. 9/2012.
.. [ZSG05] *W. Zhang, S. Shan, W. Gao, X. Chen and H. Zhang*. **Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition**. Computer Vision, IEEE International Conference on, 1:786-791, 2005.
-.. [MM09] *C. McCool, S. Marcel*. **Parts-based face verification using local frequency bands**. In Advances in biometrics, volume 5558 of Lecture Notes in Computer Science. 2009.
-.. .. [WMM12] *R. Wallace, M. McLaren, C. McCool and S. Marcel*. **Cross-pollination of normalisation techniques from speaker to face authentication using Gaussian mixture models**. IEEE Transactions on Information Forensics and Security, 2012.
.. [WMM11] *R. Wallace, M. McLaren, C. McCool and S. Marcel*. **Inter-session variability modelling and joint factor analysis for face authentication**. International Joint Conference on Biometrics. 2011.
-.. [Pri07] *S. J. D. Prince*. **Probabilistic linear discriminant analysis for inferences about identity**. Proceedings of the International Conference on Computer Vision. 2007.
-.. [ESM13] *L. El Shafey, Chris McCool, Roy Wallace and Sébastien Marcel*. **A scalable formulation of probabilistic linear discriminant analysis: applied to face recognition**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7):1788-1794, 7/2013.
-.. [WM12] *R. Wallace and M. McLaren*. **Total variability modelling for face verification**. IET Biometrics, vol.1, no.4, 188-199, 12/2012
.. [TT10] *X. Tan and B. Triggs*. **Enhanced local texture feature sets for face recognition under difficult lighting conditions**. IEEE Transactions on Image Processing, 19(6):1635-1650, 2010.
.. [WLW04] *H. Wang, S.Z. Li and Y. Wang*. **Face recognition under varying lighting conditions using self quotient image**. In IEEE International Conference on Automatic Face and Gesture Recognition (AFGR), pages 819-824. 2004.
-.. .. [HRM06] *G. Heusch, Y. Rodriguez, and S. Marcel*. **Local Binary Patterns as an Image Preprocessing for Face Authentication**. In IEEE International Conference on Automatic Face and Gesture Recognition (AFGR), 2006.
.. [WFK97] *L. Wiskott, J.-M. Fellous, N. Krüger and C.v.d. Malsburg*. **Face recognition by elastic bunch graph matching**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:775-779, 1997.
.. [ZSQ09] *W. Zhang, S. Shan, L. Qing, X. Chen and W. Gao*. **Are Gabor phases really useless for face recognition?** Pattern Analysis & Applications, 12:301-307, 2009.
-.. [GW09] *M. Günther and R.P. Würtz*. **Face detection and recognition using maximum likelihood classifiers on Gabor graphs**. International Journal of Pattern Recognition and Artificial Intelligence, 23(3):433-461, 2009.
-.. .. [GWM12] *M. Günther, R. Wallace and S. Marcel*. **An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms**. Computer Vision - ECCV 2012. Workshops and Demonstrations, LNCS, 7585, 547-556, 2012.
+.. [TFP18] de Freitas Pereira, Tiago, André Anjos, and Sébastien Marcel. "Heterogeneous face recognition using domain specific units." IEEE Transactions on Information Forensics and Security 14.7 (2018): 1803-1816.
+.. [HRM06] *G. Heusch, Y. Rodriguez, and S. Marcel*. **Local Binary Patterns as an Image Preprocessing for Face Authentication**. In IEEE International Conference on Automatic Face and Gesture Recognition (AFGR), 2006.