Bob 2.0 implementation of ISV training (U and D subspaces)
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 |
|---|---|---|
| ubm | tutorial/gmm/1 | Input |
| statistics | tutorial/gmm_statistics/1 | Input |
| client_id | system/text/1 | Input |
| isvbase | tpereira/isvbase/1 | Output |
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
| Name | Description | Type | Default | Range/Choices |
|---|---|---|---|---|
| isv-training-iterations | uint32 | 10 | ||
| init-seed | uint32 | 0 | ||
| subspace-dimension-of-u | uint32 | 50 | ||
| relevance-factor | float64 | 4.0 |
xxxxxxxxxximport bob.learn.emimport bob.coreimport numpydef gmm_from_data(data): """Reads a bob.learn.em.GMMMachine from a BEAT Data object""" dim_c, dim_d = 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 gmmdef stats_from_data(data): """Reads a bob.learn.em.GMMStats from a BEAT Data object""" dim_c, dim_d = data.sum_px.shape stat = bob.learn.em.GMMStats(dim_c, dim_d) stat.t = int(data.t) stat.n = data.n stat.sum_px = data.sum_px stat.sum_pxx = data.sum_pxx return statclass Algorithm: def __init__(self): self.isv_training_iterations = 10 self.relevance_factor = 4. self.subspace_dimension_of_u = 50 self.init_seed = 0 self.data = {} self.ubm = None def setup(self, parameters): self.isv_training_iterations = parameters.get('isv-training-iterations', self.isv_training_iterations) self.relevance_factor = parameters.get('relevance-factor', self.relevance_factor) self.subspace_dimension_of_u = parameters.get('subspace-dimension-of-u', self.subspace_dimension_of_u) self.init_seed = parameters.get('init-seed', self.init_seed) return True def process(self, inputs, outputs): # retrieve the UBM once if self.ubm is None: self.ubm = gmm_from_data(inputs['ubm'].data) stats = stats_from_data(inputs["statistics"].data) c_id = inputs["client_id"].data.text if c_id in self.data.keys(): self.data[c_id].append(stats) else: self.data[c_id] = [stats] if not(inputs.hasMoreData()): # create array set used for training training_set = [v for k,v in self.data.iteritems()] isvbase = bob.learn.em.ISVBase(self.ubm, int(self.subspace_dimension_of_u)) trainer = bob.learn.em.ISVTrainer(self.relevance_factor) rng = bob.core.random.mt19937(int(self.init_seed)) bob.learn.em.train(trainer, isvbase, training_set, max_iterations=int(self.isv_training_iterations), rng=rng) # outputs data outputs["isvbase"].write({ 'subspace_u': isvbase.u, 'subspace_d': isvbase.d, }) return TrueThe code for this algorithm in Python
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For a Gaussian Mixture Models (GMM) mean supervector space, computes the within-class variability subspace (U subspace) described in [McCool2013]:
This algorithm relies on the Bob library.
The inputs are:
The outputs are subspace_u and subspace_d for the session and the client offset respectivelly.
| [McCool2013] |
|
| Updated | 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_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/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/isv-speaker-verification-spoof/1/isv-speaker-verification-spoof-pa | avspoof/2@physicalaccess_verification,avspoof/2@physicalaccess_verification_spoof | 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 |
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.