Bob 2.0 implementation of the biometrics evaluation metrics for anti-spoofing methods
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.
Analyzers may produce any number of results. Once experiments using this analyzer are done, you may display the results or filter experiments using criteria based on them.
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
|Number of histogram bins for negative scores
|Number of histogram bins for positive scores
The code for this algorithm in Python
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An algorithm that implements standard metrics for antispoofing evaluation.
Specifically, it outputs, for the development set, the following quantities:
And for the test set:
This implementation relies on the &#39;measure&#39; package from the `Bob &lt;http://www.idiap.ch/software/bob&gt;`_ library. In particular, you may check the package `bob.measure &lt;http://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/measure/&gt;`_ for more details.
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.