Computes the GMM Statistics

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: main

Endpoint Name Data Format Nature
features system/array_2d_floats/1 Input
statistics tutorial/gmm_statistics/1 Output

Group: train

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

The code for this algorithm in Python
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For a given set of feature vectors and a Gaussian Mixture Model (GMM), this algorithm computes the 0th, 1st and 2nd order GMM Statistics (Baum-Welch) relying on Bob implementation.

This algorithm relies on the Bob library.

The inputs are:

  • features: A set of floating point vectors as a two-dimensional array (64 bits) of a client. The number of rows correspond to the number of samples, and the number of columns to the dimensionality of the samples.
  • ubm: A GMM corresponding to the Universal Background Model.

The output are the statistics of the GMM of a given set of feature vectors (MAP adaptation).

Experiments

Updated Name Databases/Protocols Analyzers
smarcel/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_bobv2-0 mobio/1@female tutorial/eerhter_postperf_iso/1
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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