BEAT - tutorial/dct/3

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 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
image	system/array_2d_uint8/1	Input
features	system/array_2d_floats/1	Output

Parameters 3

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name	Type	Default
block-size	uint32	12
block-overlap	uint32	11
number-of-components	uint32	45

import bob
import numpy

class Algorithm:

def __init__(self):
        self.block_size = 12
        self.block_overlap = 11
        self.number_of_components = 45

def setup(self, parameters):
        self.block_size = parameters.get('block-size', self.block_size)
        self.block_overlap = parameters.get('block-overlap', self.block_overlap)
        self.number_of_components = parameters.get('number-of-components',
                                                   self.number_of_components)

self.extractor = bob.ip.DCTFeatures(
            int(self.block_size),
            int(self.block_size),
            int(self.block_overlap),
            int(self.block_overlap),
            int(self.number_of_components),
        )

return True

def process(self, inputs, outputs):
        float_image = inputs["image"].data.value.astype('float64') / 255.0

outputs["features"].write({
            'value': numpy.vstack(self.extractor(float_image))
        })

return True

The code for this algorithm in Python
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Extract DCT features for the parts-based Face Recognition System described in [McCool2009], [McCool2013].

This algorithm relies on the Bob library.

The input, image, is a two-dimensional array of floats (64 bits) corresponding to one image. The outputs, features, is a two-dimensional array of floats (64 bits) corresponding to the DCT coeficients of each block.

[McCool2009]

McCool, Christopher, and Sebastien Marcel: Parts-based face verification using local frequency bands. Advances in Biometrics. Springer Berlin Heidelberg, 2009. 259-268.

[McCool2013]

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_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
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 dct 3

Endpoint Groups 1

Unnamed group

Parameters 3

Experiments

algorithms

tutorial

dct

3