Bob 2.0 training of two GMMs for two types of features

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

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
class system/text/1 Input
classifier pkorshunov/two-classes-gmm/1 Output

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

Name Description Type Default Range/Choices
number-of-gaussians The number of Gaussian Components uint32 100
maximum-number-of-iterations The maximum number of iterations for the EM algorithm uint32 10

The code for this algorithm in Python
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Implements a GMM-based training, each GMM model for each of two types of data

Experiments

Updated Name Databases/Protocols Analyzers
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/speech-pad-simple/1/speech-pad_gmm-pa avspoof/2@physicalaccess_antispoofing pkorshunov/simple_antispoofing_analyzer/4

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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