BEAT - pkorshunov/eerhter_postperf_iso

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 3

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	elie_khoury/string_probe_scores/1	Input

Group: dev

Endpoint Name	Data Format	Nature
scores_dev	elie_khoury/string_probe_scores/1	Input

Group: test_attacks

Endpoint Name	Data Format	Nature
scores_test_attacks	elie_khoury/string_probe_scores/1	Input

Results 13

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
far_test	float32
number_of_negatives_dev	int32
frr_test	float32
far_dev	float32
eer	float32
number_of_negatives_test	int32
roc_test	plot/isoroc/1
roc_dev	plot/isoroc/1
threshold	float32
frr_dev	float32
hter	float32
number_of_positives_test	int32
number_of_positives_dev	int32

import bob.measure
import numpy

class Algorithm:

def __init__(self):
        self.positives_dev = None
        self.negatives_dev = []
        self.positives_test = []
        self.negatives_test = None

def process(self, inputs, output):

# accumulate the dev scores
        if self.positives_dev is None:
            self.positives_dev = []
            while inputs['scores_dev'].hasMoreData():
                inputs['scores_dev'].next()
                data_dev = inputs['scores_dev'].data
                self.positives_dev.extend([k.score for k in data_dev.scores if k.template_identity == data_dev.client_identity])
                self.negatives_dev.extend([k.score for k in data_dev.scores if k.template_identity != data_dev.client_identity])

if self.negatives_test is None:
            self.negatives_test = []
            while inputs['scores_test_attacks'].hasMoreData():
                inputs['scores_test_attacks'].next()
                data_dev = inputs['scores_test_attacks'].data
                # attacks are negative set for the test set
                self.negatives_test.extend([k.score for k in data_dev.scores if k.template_identity == data_dev.client_identity])
                
        # accumulate the test scores
        data_test = inputs['scores_test'].data
        # we do not consider zero-impostors for test set, hence we take only positive set here
        self.positives_test.extend([k.score for k in data_test.scores if k.template_identity == data_test.client_identity])

# once all values are received, evaluate the scores
        if not(inputs.hasMoreData()):
            eer_threshold = bob.measure.eer_threshold(self.negatives_dev, self.positives_dev)
            far_dev, frr_dev   = bob.measure.farfrr(self.negatives_dev, self.positives_dev, eer_threshold)
            far_test, frr_test = bob.measure.farfrr(self.negatives_test, self.positives_test, eer_threshold)
            roc_points_dev     = bob.measure.roc(self.negatives_dev, self.positives_dev, 100)
            roc_points_test    = bob.measure.roc(self.negatives_test, self.positives_test, 100)

# writes the output back to the platform
            output.write({
                'eer': numpy.float32((far_dev+frr_dev) / 2.),
                'hter': numpy.float32((far_test+frr_test) / 2.),
                'far_dev': numpy.float32(far_dev),
                'frr_dev': numpy.float32(frr_dev),
                'far_test': numpy.float32(far_test),
                'frr_test': numpy.float32(frr_test),
                'number_of_positives_dev': numpy.int32(len(self.positives_dev)),
                'number_of_negatives_dev': numpy.int32(len(self.negatives_dev)),
                'number_of_positives_test': numpy.int32(len(self.positives_test)),
                'number_of_negatives_test': numpy.int32(len(self.negatives_test)),
                'threshold': numpy.float32(eer_threshold),
                'roc_dev': {
                    "data":
                        [
                            {
                                "label": "roc",
                                "false_positives": roc_points_dev[0],
                                "false_negatives": roc_points_dev[1],
                                "number_of_positives": numpy.uint64(len(self.positives_dev)),
                                "number_of_negatives": numpy.uint64(len(self.negatives_dev)),
                            }
                        ]
                    },
                'roc_test': {
                    "data":
                        [
                            {
                                "label": "roc",
                                "false_positives": roc_points_test[0],
                                "false_negatives": roc_points_test[1],
                                "number_of_positives": numpy.uint64(len(self.positives_test)),
                                "number_of_negatives": numpy.uint64(len(self.negatives_test)),
                            }
                        ]
                    }
            })

return True

The code for this algorithm in Python
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An algorithm that implements standard metrics for biometric system evaluation.

Specifically, it returns:

eer: the equal error rate (EER) on the development set.
hter: the half total error rate (HTER) on the test set
far_dev: the false acceptance rate (FAR) on the development set
frr_dev: the false rejection rate (FRR) on the development set
far_dev: the false acceptance rate (FAR) on the test set
frr_test: the false rejection rate (FRR) on the test set
number_of_positives_dev: the number of positive (genuine) trials on the development set
number_of_negatives_dev: the number of negative (impostor) trials on the development set
number_of_positives_test: the number of positive (genuine) trials on the test set
number_of_negatives_test: the number of negative (impostor) trials on the test set
threshold: the threshold at the equal error rate on the development set
roc_dev: the receiver operating characteristic (ROC) curve on the development set according to the biometrics standard ISO/IEC 19795-1:2006(E)
roc_test: the receiver operating characteristic (ROC) curve on the test set according to the biometrics standard ISO/IEC 19795-1:2006(E)

This implementation relies on the 'measure' package from the Bob library.

Experiments

Attestation:

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Status:

			Updated	Name	Databases/Protocols	Analyzers
				pkorshunov/pkorshunov/isv-speaker-verification-spoof/1/isv-speaker-verification-spoof-pa	avspoof/2@physicalaccess_verification_spoof,avspoof/2@physicalaccess_verification	pkorshunov/eerhter_postperf_iso_spoof/1

Scientific Python 2.7 (0.1.0) 3

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 pkorshunov eerhter_postperf_iso_spoof 1