* ``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>`_
* ``arcface-insightface``: Arcface model (Resnet100 backbone) from `Insightface <https://github.com/deepinsight/insightface>`_
* ``resnet50-msceleb-arcface-2021``: Resnet Arcface model trained with MSCeleb dataset (dataset partially prunned)
Deep Learning with different interfaces baselines
=================================================
* ``resnet50-msceleb-arcface-20210521``: Arcface model trained with MSCeleb dataset (dataset prunned)
* ``mxnet-pipe``: Arcface Resnet Model using MxNet Interfaces from `Insightface <https://github.com/deepinsight/insightface>`_
* ``resnet50-vgg2-arcface-2021``: Arcface model trained with VGG2 dataset
* ``mxnet-tinyface``: Applying `tinyface annoator <https://github.com/chinakook/hr101_mxnet>`_ for the Arcface Resnet Model using MxNet Interfaces from `Insightface <https://github.com/deepinsight/insightface>`_
* ``iresnet34``: Arcface model (Resnet 34 backbone) from `Pytorch InsightFace <https://github.com/nizhib/pytorch-insightface>`_
* ``iresnet50``: Arcface model (Resnet 50 backbone) from `Pytorch InsightFace <https://github.com/nizhib/pytorch-insightface>`_
* ``iresnet100``: Arcface model (Resnet 100 backbone) from `Pytorch InsightFace <https://github.com/nizhib/pytorch-insightface>`_
* ``pytorch-pipe-v1``: Pytorch network that extracts 1000-dimensional features, trained by Manual Gunther, as described in [LGB18]_
* ``vgg16-oxford``: VGG16 Face model from `Oxford <https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/>`_
* ``pytorch-pipe-v2``: Inception Resnet face recognition model from `facenet_pytorch <https://github.com/timesler/facenet-pytorch>`_
* ``tf-pipe``: Inception Resnet v2 model trained using the MSCeleb dataset in the context of the work published by [TFP18]_
* ``opencv-pipe``: VGG Face descriptor pretrained models, i.e. `Caffe model <https://www.robots.ox.ac.uk/~vgg/software/vgg_face/>`_
* ``afffe``: Pytorch network that extracts 1000-dimensional features, trained by Manual Gunther, as described in [LGB18]_
The MOBIO dataset is a video database containing bimodal data (face/speaker).
It is composed by 152 people (split in the two genders male and female), mostly Europeans, split in 5 sessions (few weeks time lapse between sessions).
The database was recorded using two types of mobile devices: mobile phones (NOKIA N93i) and laptop
computers(standard 2008 MacBook).
For face recognition images are used instead of videos.
One image was extracted from each video by choosing the video frame after 10 seconds.
The eye positions were manually labelled and distributed with the database.
For more information check:
.. code-block:: latex
@article{McCool_IET_BMT_2013,
title = {Session variability modelling for face authentication},
author = {McCool, Chris and Wallace, Roy and McLaren, Mitchell and El Shafey, Laurent and Marcel, S{\'{e}}bastien},
month = sep,
journal = {IET Biometrics},
volume = {2},
number = {3},
year = {2013},
pages = {117-129},
issn = {2047-4938},
doi = {10.1049/iet-bmt.2012.0059},
}
Benchmarks
==========
You can run the mobio baselines command with a simple command such as:
.. code-block:: bash
bob bio pipeline vanilla-biometrics mobio-male arcface-insightface
Scores from some of our baselines can be found `here <https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/scores/mobio-male.tar.gz>`_.
A det curve can be generated with these scores by running the following commands:
bob bio det ./mobio-male/{arcface_insightFace_lresnet100,inception_resnet_v2_msceleb_centerloss_2018,iresnet50,iresnet100,mobilenetv2_msceleb_arcface_2021,resnet50_msceleb_arcface_20210521,vgg16_oxford_baseline,afffe_baseline}/scores-{dev,eval} --legends arcface_insightFace_lresnet100,inception_resnet_v2_msceleb_centerloss_2018,iresnet50,iresnet100,mobilenetv2_msceleb_arcface_2021,resnet50_msceleb_arcface_20210521,vgg16_oxford_baseline,afffe -S -e --figsize 16,8
and get the following :download:`plot <./plots/det-mobio-male.pdf>`.
