Evaluation of a binary anti-spoofing system

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 an analyzer. It can only be used on analysis blocks.

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: test

Endpoint Name Data Format Nature
scores_test system/float/1 Input
label_test system/text/1 Input

Group: dev

Endpoint Name Data Format Nature
scores_dev system/float/1 Input
label_dev system/text/1 Input

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.

Name Type
test_frr float32
test_positives int32
dev_eer_threshold float32
dev_positives int32
dev_roc plot/isoroc/1
test_hter float32
dev_negatives int32
test_scores plot/bar/1
dev_scores plot/bar/1
dev_far float32
test_roc plot/isoroc/1
test_negatives int32
test_far float32
dev_frr float32
dev_eer float32

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:

  • eer: the equal error rate
  • threshold: the threshold at the equal error rate
  • dev_far: the false acceptance rate
  • dev_frr: the false rejection rate
  • dev_positives: the number of genuine samples
  • dev_negatives: the number of spoofing attack samples
  • dev_roc: the receiver operating characteristic curve
  • dev_scores: the score distribution for all samples on the set

And for the test set:

  • hter: the half total error rate, calculated on the EER
  • test_far: the false acceptance rate
  • test_frr: the false rejection rate
  • test_positives: the number of genuine samples
  • test_negatives: the number of spoofing attack samples
  • test_roc: the receiver operating characteristic curve
  • test_scores: the score distribution for all samples on the set

This implementation relies on the 'measure' package from the Bob library. In particular, you may check the package bob.measure for more details.


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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