Creates a histogram model for a group of samples by averaging their feature vector histograms

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.

Unnamed group

Endpoint Name Data Format Nature
labels system/text/1 Input
feature_vectors system/array_1d_floats/1 Input
model_hist system/array_1d_floats/1 Output

The code for this algorithm in Python
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This algorithm creates a histogram model for a group of samples. For example, these samples can be real od attack samples in an anti-spoofing scenario. The input assumes sample feature vectors which are histograms. The model is created simpy by averaging the input histograms.


Updated Name Databases/Protocols Analyzers
sbhatta/ivana7c/simple-antispoofing-updated/1/replay2-antispoofing-lbp-histograms replay/2@grandtest sbhatta/iqm_spoof_eer_analyzer/9
smarcel/ivana7c/simple-antispoofing-updated/1/antispoof-chi2-expA-rr22 replay/1@countermeasure ivana7c/spoofing_eer/1
ivana7c/ivana7c/simple-antispoofing-updated/1/antispoof-chi2-expA replay/1@countermeasure ivana7c/spoofing_eer/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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