Bob 2.0 implementation of ISV scoring. All input ids are strings.
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 |
|---|---|---|
| probe_isv_offset | system/array_1d_floats/1 | Input |
| probe_statistics | tutorial/gmm_statistics/1 | Input |
| template_ids | system/array_1d_text/1 | Input |
| probe_id | system/text/1 | Input |
| probe_client_id | system/text/1 | Input |
| scores | elie_khoury/string_probe_scores/1 | Output |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| template_model | tpereira/isvmachine/1 | Input |
| template_client_id | system/text/1 | Input |
| template_id | system/text/1 | Input |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| ubm | tutorial/gmm/1 | Input |
| isvbase | tpereira/isvbase/1 | Input |
xxxxxxxxxximport bob.learn.emimport 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 isvbase_from_data(data, ubm): """Reads a bob.learn.em.ISVBase from a BEAT Data object""" dim_cd, dim_u = data.subspace_u.shape isvbase = bob.learn.em.ISVBase(ubm, dim_u) isvbase.u = data.subspace_u isvbase.d = data.subspace_d return isvbasedef 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 statdef isvmachine_from_data(data, isvbase): """Reads a bob.learn.em.ISVBase from a BEAT Data object""" isvmachine = bob.learn.em.ISVMachine(isvbase) isvmachine.z = data.latent_z return isvmachineclass Algorithm: def __init__(self): self.ubm = None self.isvbase = None self.templates = None def process(self, inputs, outputs): # retrieve the UBM once if self.ubm is None: inputs['ubm'].next() self.ubm = gmm_from_data(inputs['ubm'].data) # retrieve the ISVBase once if self.isvbase is None: inputs['isvbase'].next() self.isvbase = isvbase_from_data(inputs['isvbase'].data, self.ubm) # retrieve all the templates once if self.templates is None: self.templates = {} group = inputs.groupOf('template_model') while group.hasMoreData(): group.next() template_id = group['template_id'].data.text self.templates[template_id] = dict( client_id = group['template_client_id'].data.text, model = isvmachine_from_data(group['template_model'].data, self.isvbase), ) # process the probe template_ids = inputs['template_ids'].data.text statistics = stats_from_data(inputs['probe_statistics'].data) ux = inputs['probe_isv_offset'].data.value scores = [] for template_id in template_ids: template_client_identity = self.templates[template_id]['client_id'] model = self.templates[template_id]['model'] score = model.forward_ux(statistics, ux) scores.append({ 'template_identity': template_client_identity, 'score': score, }) outputs['scores'].write({ 'client_identity': inputs['probe_client_id'].data.text, 'scores': scores } ) return TrueThe code for this algorithm in Python
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Computes the ISV scoring.
Specific details can be found in Equation (40) in [McCool2013].
This algorithm relies on the Bob library.
The inputs are:
The output are the scores.
| [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.