Bob 2.0 projection of features on GMM model. Input id is a string.
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 |
| scores | system/float/1 | Output |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| classifier | pkorshunov/two-classes-gmm/1 | Input |
xxxxxxxxxximport numpyimport bob.learn.emdef gmm_from_data(data): """Loads a bob.learn.em.GMMMachine from a BEAT Data object""" dim_c, dim_d = data.means.shape# print("GMM shape: %s" % str(data.means.shape)) gmm = bob.learn.em.GMMMachine(dim_c, dim_d) gmm.weights = data.weights gmm.means = data.means gmm.variances = data.variances gmm.variance_thresholds = data.variance_thresholds return gmmclass Algorithm: def __init__(self): self.gmm_one = None self.gmm_two = None def process(self, inputs, outputs): # retrieve the classifier (with two GMM models) once if self.gmm_one is None: inputs['classifier'].next() two_gmms = inputs['classifier'].data self.gmm_one = gmm_from_data(two_gmms.model_one) self.gmm_two = gmm_from_data(two_gmms.model_two) # read the features and project on both GMMs projection_one = 0 projection_two = 0 features = inputs['features'].data.value for feature in features: # project the feature on both GMMs, result of projection is a log likelihood projection_one += self.gmm_one.log_likelihood(feature) projection_two += self.gmm_two.log_likelihood(feature) projection_one /= features.shape[0] projection_two /= features.shape[0] # compute the score as the difference between two average log-likelihoods score = projection_one - projection_two # output the projection score outputs['scores'].write({ 'value': score }) return TrueThe code for this algorithm in Python
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For a given set of feature vectors and a Gaussian Mixture Models (GMM), this algorithm implements the Maximum-a-posteriori (MAP) estimation (adapting only the means).
Details of MAP estimation can be found in the paper: Reynolds, Douglas A., Thomas F. Quatieri, and Robert B. Dunn. "Speaker verification using adapted Gaussian mixture models." Digital signal processing 10.1 (2000): 19-41. A very good description on how the MAP estimation works can be found in the `Mathematical Monks's <https://www.youtube.com/watch?v=kkhdIriddSI&index=31&list=PLD0F06AA0D2E8FFBA&spfreload=1>`_ YouTube channel.z
This algorithm relies on the `Bob <http://www.idiap.ch/software/bob/>`_ library.
| 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.