BEAT - pkorshunov/gmm/1

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

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
features	system/array_2d_floats/1	Input
ubm	tutorial/gmm/1	Output

Parameters 2

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

Name	Description	Type	Default	Range/Choices
maximum-number-of-iterations	The maximum number of iterations for the EM algorithm	uint32	10
number-of-gaussians	The number of Gaussian Components	uint32	100

import numpy
import bob.learn.em

class Algorithm:

def __init__(self):
        self.number_of_gaussians = 100
        self.max_iterations = 10
        self.data = []

def setup(self, parameters):
        self.number_of_gaussians = parameters.get('number-of-gaussians',
                                                  self.number_of_gaussians)

self.max_iterations = parameters.get('maximum-number-of-iterations',
                                             self.max_iterations)

return True

def process(self, inputs, outputs):
        self.data.append(inputs["features"].data.value)

if not(inputs.hasMoreData()):
            # create array set used for training
            training_set = numpy.vstack(self.data)

input_size = training_set.shape[1]

# create the KMeans and UBM machine
            kmeans = bob.learn.em.KMeansMachine(int(self.number_of_gaussians), input_size)
            ubm = bob.learn.em.GMMMachine(int(self.number_of_gaussians), input_size)

# create the KMeansTrainer
            kmeans_trainer = bob.learn.em.KMeansTrainer('RANDOM_NO_DUPLICATE')
            #kmeans_trainer.initialization_method = bob.learn.em.KMeansTrainer.RANDOM_NO_DUPLICATE
            #kmeans_trainer.max_iterations = int(self.max_iterations)

# train using the KMeansTrainer
            bob.learn.em.train(kmeans_trainer, kmeans, training_set, int(self.max_iterations))
            #kmeans_trainer.train(kmeans, training_set)

(variances, weights) = kmeans.get_variances_and_weights_for_each_cluster(training_set)
            means = kmeans.means

# initialize the GMM
            ubm.means = means
            ubm.variances = variances
            ubm.weights = weights

# train the GMM
            trainer = bob.learn.em.ML_GMMTrainer()
            #trainer.max_iterations = int(self.max_iterations)
            #trainer.train(ubm, training_set)

bob.learn.em.train(trainer, ubm, training_set, int(self.max_iterations))

# outputs data
            outputs["ubm"].write({
                'weights':              ubm.weights,
                'means':                ubm.means,
                'variances':            ubm.variances,
                'variance_thresholds':  ubm.variance_thresholds,
            })

return True

The code for this algorithm in Python
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Implements a GMM-based UBM training

Experiments

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Status:

Name	Databases/Protocols	Analyzers
pkorshunov/pkorshunov/isv-asv-pad-fusion-complete/1/asv_isv-pad_lbp_hist_ratios_lr-fusion_lr-pa_aligned	avspoof/2@physicalaccess_verify_train,avspoof/2@physicalaccess_verification,avspoof/2@physicalaccess_verification_spoof,avspoof/2@physicalaccess_verify_train_spoof,avspoof/2@physicalaccess_antispoofing	pkorshunov/spoof-score-fusion-roc_hist/1
pkorshunov/pkorshunov/isv-asv-pad-fusion-complete/1/asv_isv-pad_gmm-fusion_lr-pa	avspoof/2@physicalaccess_verify_train,avspoof/2@physicalaccess_verification,avspoof/2@physicalaccess_verification_spoof,avspoof/2@physicalaccess_verify_train_spoof,avspoof/2@physicalaccess_antispoofing	pkorshunov/spoof-score-fusion-roc_hist/1
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
pkorshunov/pkorshunov/isv-speaker-verification/1/isv-speaker-verification-licit	avspoof/2@physicalaccess_verification	pkorshunov/eerhter_postperf_iso/1

Scientific Python 2.7 (0.1.0) 29

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 gmm 1

Endpoint Groups 1

Unnamed group

Parameters 2

Experiments

algorithms

pkorshunov

gmm

1