Computes the ISV scoring

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.
This algorithm is splittable

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: probes

Endpoint Name Data Format Nature
probe_isv_offset system/array_1d_floats/1 Input
probe_statistics tutorial/gmm_statistics/1 Input
template_ids system/array_1d_uint64/1 Input
probe_id system/uint64/1 Input
probe_client_id system/uint64/1 Input
scores tutorial/probe_scores/1 Output

Group: templates

Endpoint Name Data Format Nature
template_client_id system/uint64/1 Input
template_id system/uint64/1 Input
template_model tpereira/isvmachine/1 Input

Group: train

Endpoint Name Data Format Nature
ubm tutorial/gmm/1 Input
isvbase tpereira/isvbase/1 Input

The code for this algorithm in Python
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Computes the ISV scoring.

Specific details can be found in Equation (40) in [McCool2013].

This algorithm relies on the Bob library.

The inputs are:

  • probe_isv_offset: The Session Offset of a probe.
  • probe_statistics: A set of GMM Statistics of a probe.
  • template_ids: A set of probe (class/subject) identifier as an unsigned 64 bits integer.
  • ubm: A GMM corresponding to the Universal Background Model.
  • isvbase: The subspace_u and subspace_d for the session and the client offset respectivelly.
  • template_id: Client (class/subject) identifier as an unsigned 64 bits integer.
  • template_model: The client model is the latent variable zi ( Eq. (31) in [McCool2013]) that corresponds to the client offset (with the session variations suppressed).

The output are the scores.

[McCool2013](1, 2) McCool, Christopher, et al. "Session variability modelling for face authentication." IET biometrics 2.3 (2013): 117-129.

Experiments

Updated Name Databases/Protocols Analyzers
tpereira/tpereira/full_isv_multi/2/btas2015_face-periocular_cpqd-smartphone-male_det cpqd/1@smartphone_male 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_multi/2/btas2015_face-periocular_mobio-male_det mobio/1@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_multi/2/btas2015_face-periocular_cpqd-smartphone-female_det cpqd/1@smartphone_female 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_multi/2/btas2015_face-periocular_mobio-female_det mobio/1@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
martabarrero/smarcel/full_isv/1/Prueba_ISV_2 banca/1@Mc tutorial/eerhter_postperf/1

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

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