Bob 2.0 implementation of the biometrics evaluation metrics for anti-spoofing methods

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

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

Name Description Type Default Range/Choices
n_bins_negatives Number of histogram bins for negative scores uint32 100
n_bins_positives Number of histogram bins for positive scores uint32 20

The code for this algorithm in Python
The ruler at 80 columns indicate suggested POSIX line breaks (for readability).
The editor will automatically enlarge to accomodate the entirety of your input
Use keyboard shortcuts for search/replace and faster editing. For example, use Ctrl-F (PC) or Cmd-F (Mac) to search through this box

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.

Experiments

Updated Name Databases/Protocols Analyzers
pkorshunov/pkorshunov/speech-antispoofing-baseline/1/btas2016-baseline-pa avspoof/1@physicalaccess_antispoofing pkorshunov/simple_antispoofing_analyzer/2

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.

Terms of Service | Contact Information | BEAT platform version 1.5.0b0 | © Idiap Research Institute - 2013-2019