To edit a toolchain, please use a modern browser (Mozilla Firefox 3.0+, Google Chrome 1+, Apple Safari 3+, Opera 9.5+, Microsoft Internet Explorer 9+)

This toolchain implements a parts-based face verification using Intersession Variability Modelling (McCool2013]) described in [McCool2013].

The toolchain is detailed as follows:

  • The images from the database are aligned according to the specified eye locations.
  • In the preprocessing step the cropped faces are basically filtered. Currently the following algorithms can be used for such purpose: Filters and Tan and Triggs
  • In the feature extraction step any local feature algorithm can be used, such as: DCT or LBP.
  • The extracted features from the training set are used to train the Universal Background Model (UBM). The algorithm GMM can be used for this purpose.
  • For each set of feature vectors of one image, the GMM statistics are extracted (based on the Maximum a Posteriori (MAP) adaption using the UBM as a prior).
    The algorithm GMM Statistics can be used for this purpose.
  • The GMM Statistics of the training set are used to estimate the U subspace. The algorithm ISV can be used for this purpose.
  • For each set of GMM Statistics of a given client, the UBM and the U subspace, a client can be enrolled using the algorithm ISV Enroll
  • The scoring step for the ISV is defined as the LLR between the client model and the UBM, using the GMM Statistics of a given probe as input. The algorithm ISV Scoring can be used for this purpuse. The main inputs for this algoritm are: the GMM Statistics of a probe, the client model, the UBM, the U subspace.
  • The analysis step integrates scores from the development and the test set.
  1. McCool, et al.: Session variability modelling for face authentication. IET biometrics 2.3 (2013): 117-129.
Updated Name Databases/Protocols Analyzers
tpereira/tpereira/full_isv/2/btas2015_periocular_mobio-female_det_bobv2-0 mobio/1@female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_periocular_cpqd-smartphone-male_det cpqd/1@smartphone_male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_cpqd-smartphone-male_det cpqd/1@smartphone_male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_periocular_mobio-male_det mobio/1@male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_mobio-male_det mobio/1@male tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_periocular_cpqd-smartphone-female_det cpqd/1@smartphone_female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_cpqd-smartphone-female_det cpqd/1@smartphone_female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_periocular_mobio-female_det mobio/1@female tutorial/eerhter_postperf_iso/1
tpereira/tpereira/full_isv/2/btas2015_face_mobio-female_det mobio/1@female tutorial/eerhter_postperf_iso/1
Terms of Service | Contact Information | BEAT platform version 2.2.1b0 | © Idiap Research Institute - 2013-2024