Compares a histogram to a histogram model using a Chi-2 distance

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
feature_vectors system/array_1d_floats/1 Input
scores system/float/1 Output

Group: train

Endpoint Name Data Format Nature
machine system/array_1d_floats/1 Input

The code for this algorithm in Python
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This algorithm compares an input histogram to a histogram model. It computes a score between the input histogram and the model using Chi-2 distance measurement.

Experiments

Updated Name Databases/Protocols Analyzers
anjos/ivana7c/simple-antispoofing-updated/1/face-antipoofing-lbp-histogram-comparison replay/1@countermeasure anjos/antispoofing_analyzer/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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