Standard metrics for biometric system evaluation

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: main

Endpoint Name Data Format Nature
scores tutorial/multiclass_probe_scores/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
number_of_negatives int32
roc plot/scatter/1
number_of_positives int32
cer float32

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 biometric system evaluation.

Specifically, it returns:

  • cer: the classification error rate (CER)
  • number_of_positives: the number of positive (genuine) trials
  • number_of_negatives: the number of negative (impostor) trials
  • roc: the receiver operating characteristic (ROC) curve

This implementation relies on the 'measure' package from the Bob library. See for more details.

No experiments are using this algorithm.
This algorithm was never executed.
Terms of Service | Contact Information | BEAT platform version 2.2.1b0 | © Idiap Research Institute - 2013-2024