BEAT - tutorial/isv

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: main

Endpoint Name	Data Format	Nature
statistics	tutorial/gmm_statistics/1	Input
isv_offset	system/array_1d_floats/1	Output

Unnamed group

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

import bob
import numpy

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

dim_c, dim_d = data.means.shape
    gmm = bob.machine.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 isvbase_from_data(data, ubm):
    """Unmangles a bob.machine.ISVBase from a BEAT Data object"""

dim_cd, dim_u = data.subspace_u.shape
    isvbase = bob.machine.ISVBase(ubm, dim_u)
    isvbase.u = data.subspace_u
    isvbase.d = data.subspace_d
    return isvbase

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

dim_c, dim_d = data.sum_px.shape
    stat = bob.machine.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.isvbase    = 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 the ISVBase once
        if self.isvbase is None:
            inputs['isvbase'].next()
            self.isvbase = isvbase_from_data(inputs['isvbase'].data, self.ubm)

stats = stats_from_data(inputs["statistics"].data)

projected_isv = numpy.ndarray(shape=(self.ubm.dim_c*self.ubm.dim_d,), dtype=numpy.float64)
        model = bob.machine.ISVMachine(self.isvbase)
        model.estimate_ux(stats, projected_isv)

# outputs data
        outputs["isv_offset"].write({
            'value':       projected_isv,
        })

return True

The code for this algorithm in Python
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Given a feature vector, a GMM and a U subspace, computes the session offset (x_i, j).

Specific details can be found in [McCool2013].

This algorithm relies on the Bob library.

The inputs are:

statistics: A set of GMM Statistics of a probe.
ubm: A GMM corresponding to the Universal Background Model.
isvbase: The subspace_u and subspace_d for the session and the client offset respectivelly.

The output, isv_offset, is the latent variable x_i, j ( Eq. (29) in [McCool2013]) that corresponds to the session offset.

[McCool2013]

(1, 2) McCool, Christopher, et al. "Session variability modelling for face authentication." IET biometrics 2.3 (2013): 117-129.

Experiments

Attestation:

Privacy:

Status:

Name	Databases/Protocols	Analyzers
smarcel/tutorial/full_isv/2/mobio_male-gmm_100Gx10I-isv_50Ux10Ix4R-dct_12Bx8Ox45C-seed101	mobio/1@male	tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_isv/2/bancaMc_isv_DCT12x8_100G_U50	banca/1@Mc	tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_isv/2/xm2vtsLp1_isv_DCT12x8_100G_U50	xm2vts/1@lp1	tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_isv/2/mobioMale_isv_DCT12x8_100G_U50	mobio/1@male	tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_isv/2/bancaP_isv_DCT12x8_100G_U50	banca/1@P	tutorial/eerhter_postperf_iso/1
tutorial/tutorial/full_isv/2/atnt_isv_DCT12x8_100G_U50	atnt/1@idiap_test_eyepos	tutorial/eerhter_postperf_iso/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 isv_offset 3

Endpoint Groups 2

Group: main

Unnamed group

Experiments

algorithms

tutorial

isv_offset

3