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 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: probes

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

Group: templates

Endpoint Name	Data Format	Nature
template_client_id	system/uint64/1	Input
template_id	system/uint64/1	Input
template_model	tutorial/gmm/1	Input

Unnamed group

Endpoint Name	Data Format	Nature
ubm	tutorial/gmm/1	Input

import bob
import numpy
from bob.machine import GMMMachine
from bob.machine import GMMStats

def gmm_from_data(data):
    """Unmangles a bob.machine.GMMMachine from a BEAT Data object"""

dim_c, dim_d = data.means.shape
    gmm = GMMMachine(dim_c, dim_d)
    gmm.weights = data.weights
    gmm.means = data.means
    gmm.variances = data.variances
    gmm.variance_thresholds = data.variance_thresholds
    return gmm

def stats_from_data(data):
    """Unmangles a bob.machine.GMMStats from a BEAT Data object"""

dim_c, dim_d = data.sum_px.shape
    stat = GMMStats(dim_c, dim_d)
    stat.t = long(data.t)
    stat.n = data.n
    stat.sum_px = data.sum_px
    stat.sum_pxx = data.sum_pxx
    return stat

class Algorithm:

def __init__(self):
        self.ubm       = None
        self.templates = None

def process(self, inputs, outputs):

# retrieve the UBM once
        if self.ubm is None:
            inputs['ubm'].next()
            self.ubm = gmm_from_data(inputs['ubm'].data)

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

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

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

self.templates[template_id] = dict(
                    client_id = group['template_client_id'].data.value,
                    model = gmm_from_data(group['template_model'].data),
                )

# process the probe
        comparison_ids = inputs['comparison_ids'].data.value
        statistics = stats_from_data(inputs['probe_statistics'].data)

scores = []
        for comparison_id in comparison_ids:
            template_client_identity = self.templates[comparison_id]['client_id']

score = bob.machine.linear_scoring([self.templates[comparison_id]['model']],
                                               self.ubm, [statistics])[0,0]

scores.append({
                'template_identity': template_client_identity,
                '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

xxxxxxxxxx
 
import bob
import numpy
from bob.machine import GMMMachine
from bob.machine import GMMStats
​
​
​
def gmm_from_data(data):
    """Unmangles a bob.machine.GMMMachine from a BEAT Data object"""
​
    dim_c, dim_d = data.means.shape
    gmm = GMMMachine(dim_c, dim_d)
    gmm.weights = data.weights
    gmm.means = data.means
    gmm.variances = data.variances
    gmm.variance_thresholds = data.variance_thresholds
    return gmm
​
​
​
def stats_from_data(data):
    """Unmangles a bob.machine.GMMStats from a BEAT Data object"""
​
    dim_c, dim_d = data.sum_px.shape
    stat = GMMStats(dim_c, dim_d)
    stat.t = long(data.t)
    stat.n = data.n
    stat.sum_px = data.sum_px
    stat.sum_pxx = data.sum_pxx
    return stat
​
​
​
class Algorithm:
​
    def __init__(self):
        self.ubm       = None
        self.templates = None
​
​
    def process(self, inputs, outputs):
​
        # retrieve the UBM once
        if self.ubm is None:
            inputs['ubm'].next()
            self.ubm = gmm_from_data(inputs['ubm'].data)
​
​
        # retrieve all the templates once
        if self.templates is None:
            self.templates = {}
            group = inputs.groupOf('template_model')
​
            while group.hasMoreData():
                group.next()
​
                template_id = group['template_id'].data.value
​
                self.templates[template_id] = dict(
                    client_id = group['template_client_id'].data.value,
                    model = gmm_from_data(group['template_model'].data),
                )
​
​
        # process the probe
        comparison_ids = inputs['comparison_ids'].data.value
        statistics = stats_from_data(inputs['probe_statistics'].data)
​
        scores = []
        for comparison_id in comparison_ids:
            template_client_identity = self.templates[comparison_id]['client_id']
​
            score = bob.machine.linear_scoring([self.templates[comparison_id]['model']],
                                               self.ubm, [statistics])[0,0]
​
            scores.append({
                'template_identity': template_client_identity,
                '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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For a given set of feature vectors, a Gaussian Mixture Model (GMM) of the target client and an UBM-GMM, this algorithm computes the scoring using the linear scoring implemented on the `Bob https://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/machine/generated/bob.machine.linear_scoring.html?highlight=linear%20scoring#bob.machine.linear_scoring`_

This algorithm relies on the Bob library.

The inputs are:

probe_statistics: A set of GMM Statistics of a probe.
probe_id: A set of probe (class/subject) identifier as an unsigned 64 bits integer.
ubm: A GMM corresponding to the Universal Background Model.
template_id: Client (class/subject) identifier as an unsigned 64 bits integer.
template_model: The GMM of the client

The output are the scores.

Docutils System Messages

System Message: ERROR/3 (<string>, line 1); backlink

Unknown target name: "bob https://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/machine/generated/bob.machine.linear_scoring.html?highlight=linear%20scoring#bob.machine.linear_scoring".

Experiments

Attestation:

Privacy:

Status:

Name	Databases/Protocols	Analyzers
tutorial/tutorial/full_ubmgmm/2/mobioMale_gmm_DCT12x8_100G	mobio/1@male	tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_ubmgmm/2/mobioMale_ubmgmm_DCT12x8_100G	mobio/1@male	tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_ubmgmm/2/bancaP_gmm_DCT12x8_100G	banca/1@P	tutorial/eerhter_postperf_iso/1

Scientific Python 2.7 (0.0.3) 14

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 gmm_scoring 3