Bob 2.0 training of two GMMs for two types of features
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.
| Endpoint Name | Data Format | Nature |
|---|---|---|
| features | system/array_2d_floats/1 | Input |
| class | system/text/1 | Input |
| classifier | pkorshunov/two-classes-gmm/1 | Output |
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
| Name | Description | Type | Default | Range/Choices |
|---|---|---|---|---|
| number-of-gaussians | The number of Gaussian Components | uint32 | 100 | |
| maximum-number-of-iterations | The maximum number of iterations for the EM algorithm | uint32 | 10 |
xxxxxxxxxximport numpyimport bob.learn.emclass Algorithm: def __init__(self): self.number_of_gaussians = 100 self.max_iterations = 10 self.positives = [] self.negatives = [] 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 create_gmm(self, training_data): input_size = training_data.shape[1] # create the KMeans and GMM machine kmeans = bob.learn.em.KMeansMachine(int(self.number_of_gaussians), input_size) gmm = bob.learn.em.GMMMachine(int(self.number_of_gaussians), input_size) # create the KMeansTrainer kmeans_trainer = bob.learn.em.KMeansTrainer('RANDOM_NO_DUPLICATE') # train using the KMeansTrainer bob.learn.em.train(kmeans_trainer, kmeans, training_data, int(self.max_iterations)) (variances, weights) = kmeans.get_variances_and_weights_for_each_cluster(training_data) means = kmeans.means # initialize the GMM gmm.means = means gmm.variances = variances gmm.weights = weights # train the GMM trainer = bob.learn.em.ML_GMMTrainer() bob.learn.em.train(trainer, gmm, training_data, int(self.max_iterations)) return gmm def process(self, inputs, outputs): # accumulates the input data in different # containers for hit or miss feature_vector = inputs["features"].data.value if inputs["class"].data.text == 'real': self.positives.append(feature_vector) else: self.negatives.append(feature_vector) if not(inputs.hasMoreData()): # create array set used for training self.positives = numpy.vstack(self.positives) self.negatives = numpy.vstack(self.negatives) gmm_real = self.create_gmm(self.positives) gmm_attacks = self.create_gmm(self.negatives) # outputs data outputs["classifier"].write({ "model_one": { 'weights': gmm_real.weights, 'means': gmm_real.means, 'variances': gmm_real.variances, 'variance_thresholds': gmm_real.variance_thresholds, }, "model_two": { 'weights': gmm_attacks.weights, 'means': gmm_attacks.means, 'variances': gmm_attacks.variances, 'variance_thresholds': gmm_attacks.variance_thresholds, }, }) return TrueThe code for this algorithm in Python
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Implements a GMM-based training, each GMM model for each of two types of data
| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| pkorshunov/pkorshunov/isv-asv-pad-fusion-complete/1/asv_isv-pad_gmm-fusion_lr-pa | avspoof/2@physicalaccess_verification,avspoof/2@physicalaccess_verify_train,avspoof/2@physicalaccess_verify_train_spoof,avspoof/2@physicalaccess_antispoofing,avspoof/2@physicalaccess_verification_spoof | pkorshunov/spoof-score-fusion-roc_hist/1 | ||||
| pkorshunov/pkorshunov/speech-pad-simple/1/speech-pad_gmm-pa | avspoof/2@physicalaccess_antispoofing | pkorshunov/simple_antispoofing_analyzer/4 |
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.