Bob 2.0 implementation of ISV client model training. Input template ids are strings.

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: templates

Endpoint Name Data Format Nature
statistics tutorial/gmm_statistics/1 Input
template_id system/text/1 Input
model tpereira/isvmachine/1 Output

Group: train

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

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name Description Type Default Range/Choices
isv-enroll-iterations uint32 1

The code for this algorithm in Python
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Given a feature vector, a GMM and a U subspace, computes the Intersession Variability Modeling (ISV) client model. Basically, this algorithm computes the latent variable zi excluding possible session factors (described by the latent variable xi, j).

Specific details can be found in [McCool2013]:

This algorithm relies on the Bob library.

The inputs are:

  • statistics: A set of GMM Statistics of a client for enrollment.
  • 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 a text string.

The output, model, is the latent variable zi (Eq. (31) in McCool2013) that corresponds to the client offset (with the session variations suppressed).

  1. McCool, et al.: Session variability modelling for face authentication. IET biometrics 2.3 (2013): 117-129.


Updated Name Databases/Protocols Analyzers
pkorshunov/pkorshunov/isv-asv-pad-fusion-complete/1/asv_isv-pad_lbp_hist_ratios_lr-fusion_lr-pa_aligned avspoof/2@physicalaccess_verify_train,avspoof/2@physicalaccess_verification,avspoof/2@physicalaccess_verification_spoof,avspoof/2@physicalaccess_verify_train_spoof,avspoof/2@physicalaccess_antispoofing pkorshunov/spoof-score-fusion-roc_hist/1
pkorshunov/pkorshunov/isv-asv-pad-fusion-complete/1/asv_isv-pad_gmm-fusion_lr-pa avspoof/2@physicalaccess_verify_train,avspoof/2@physicalaccess_verification,avspoof/2@physicalaccess_verification_spoof,avspoof/2@physicalaccess_verify_train_spoof,avspoof/2@physicalaccess_antispoofing pkorshunov/spoof-score-fusion-roc_hist/1
pkorshunov/pkorshunov/isv-speaker-verification-spoof/1/isv-speaker-verification-spoof-pa avspoof/2@physicalaccess_verification_spoof,avspoof/2@physicalaccess_verification pkorshunov/eerhter_postperf_iso_spoof/1
pkorshunov/pkorshunov/isv-speaker-verification/1/isv-speaker-verification-licit avspoof/2@physicalaccess_verification pkorshunov/eerhter_postperf_iso/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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