BEAT - tutorial/histogram

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 splittable

Endpoint Groups 2

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: probes

Endpoint Name	Data Format	Nature
comparison_ids	system/array_1d_uint64/1	Input
probe	system/array_1d_floats/1	Input
probe_id	system/uint64/1	Input
probe_client_id	system/uint64/1	Input
scores	tutorial/probe_scores/1	Output

Group: models

Endpoint Name	Data Format	Nature
model_id	system/uint64/1	Input
model	system/array_1d_floats/1	Input
model_client_id	system/uint64/1	Input

import bob

class Algorithm:

def __init__(self):
        self.models = None

def process(self, inputs, outputs):

# retrieve all the templates once
        if self.models is None:
            self.models = {}
            group = inputs.groupOf('model')

while group.hasMoreData():
                group.next()

model_id = group['model_id'].data.value

self.models[model_id] = {
                    'client_id' : group['model_client_id'].data.value,
                    'model' : group['model'].data.value,
                }

# process the probe
        comparison_ids = inputs['comparison_ids'].data.value
        probe = inputs['probe'].data.value

scores = []
        for comparison_id in comparison_ids:
            model_client_id = self.models[comparison_id]['client_id']

score = - bob.math.chi_square(self.models[comparison_id]['model'],
                probe)

scores.append({
                'template_identity': model_client_id,
                'score': score,
            })

outputs['scores'].write(
          {
            'client_identity': inputs['probe_client_id'].data.value,
            'scores': scores
          },
          end_data_index=inputs['probe_id'].data_index_end
        )

return True

The code for this algorithm in Python
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This algorithm computes the similarity between two histograms using bob.math.chi_square. For two given histograms H and M, the χ² distance is defined as:

χ²(H, M) = ⎲⎳_i((H_i − M_i)²)/((H_i + M_i))

Since χ² is a distance measure, but scores should be similarity values (with higher values being better), the negative χ² distance is actually returned.

Experiments

Attestation:

Privacy:

Status:

Name	Databases/Protocols	Analyzers
tpereira/tutorial/full_lbphs/2/btas2015_LBPBaseline_face_cpqd-smartphone-male_det	cpqd/1@smartphone_male	tutorial/eerhter_postperf_iso/1
tpereira/tutorial/full_lbphs/2/btas2015_LBP-baseline_periocular_mobio-male_det	mobio/1@male	tutorial/eerhter_postperf_iso/1
tpereira/tutorial/full_lbphs/2/btas2015_LBPBaseline_face_cpqd-smartphone-female_det	cpqd/1@smartphone_female	tutorial/eerhter_postperf_iso/1
tpereira/tutorial/full_lbphs/2/btas2015_LBP-baseline_periocular_mobio-female_det	mobio/1@female	tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_lbphs/2/mobioMale_lbphs12x8	mobio/1@male	tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_lbphs/2/atnt_lbphs12x8	atnt/1@idiap_test_eyepos	tutorial/eerhter_postperf_iso/1

Scientific Python 2.7 (0.0.3) 20

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 histogram_similarity 2

Endpoint Groups 2

Group: probes

Group: models

Experiments

algorithms

tutorial

histogram_similarity

2