BEAT - tutorial/postperf/1

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.

Endpoint Groups 1

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.

Unnamed group

Endpoint Name	Data Format	Nature
scores	tutorial/probe_scores/1	Input

Results 7

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
frr	float32
far	float32
roc	plot/scatter/1
eer	float32
number_of_positives	int32
threshold	float32
number_of_negatives	int32

import bob
import bob.measure
import numpy

class Algorithm:

def __init__(self):
        self.positives = []
        self.negatives = []

def process(self, inputs, output):

# accumulate the scores
        data = inputs['scores'].data

self.positives.extend([k.score for k in data.scores if k.template_identity == data.client_identity])
        self.negatives.extend([k.score for k in data.scores if k.template_identity != data.client_identity])

# once all values are received, evaluate the scores
        if not(inputs.hasMoreData()):
            eer_threshold = bob.measure.eer_threshold(self.negatives, self.positives)
            far, frr      = bob.measure.farfrr(self.negatives, self.positives, eer_threshold)
            roc_points    = bob.measure.roc(self.negatives, self.positives, 100)

# writes the output back to the platform
            output.write({
                'eer': numpy.float32((far+frr) / 2.),
                'far': numpy.float32(far),
                'frr': numpy.float32(frr),
                'threshold': numpy.float32(eer_threshold),
                'number_of_positives': numpy.int32(len(self.positives)),
                'number_of_negatives': numpy.int32(len(self.negatives)),
                'roc': {
                    "data":
                        [   
                            {   
                                "label": "roc",
                                "x": roc_points[0],
                                "y": roc_points[1],
                            }   
                        ]   
                    },
            })
        return True

The code for this algorithm in Python
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Could not find any documentation for this object.

Experiments

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Status:

Name	Databases/Protocols	Analyzers
teodors/teodors/datatype_problem/1/example_aggregation_as_lists_repacking_variables	atnt/3@idiap	tutorial/postperf/1
AntonioCandia/AntonioCandia/eigenface_preprop/1/eigenface-with-10-components	atnt/1@idiap	tutorial/postperf/1
anjos/tutorial/eigenface/1/demo42	atnt/1@idiap	tutorial/postperf/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.

algorithms tutorial postperf 1

Endpoint Groups 1

Unnamed group

Results 7

Experiments

algorithms

tutorial

postperf

1