This algorithm computes the score given a GMM and UBM using the Linear Scoring
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 |
|---|---|---|
| comparison_ids | system/array_1d_text/1 | Input |
| probe_statistics | tutorial/gmm_statistics/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_client_id | system/text/1 | Input |
| template_id | system/text/1 | Input |
| template_model | tutorial/gmm/1 | Input |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| ubm | tutorial/gmm/1 | Input |
xxxxxxxxxximport bobimport numpyfrom bob.machine import GMMMachinefrom bob.machine import GMMStatsdef gmm_from_data(data): """Unmangles a bob.machine.GMMMachine from a BEAT Data object""" dim_c, dim_d = data.means.shape gmm = 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): """Unmangles a bob.machine.GMMStats from a BEAT Data object""" dim_c, dim_d = data.sum_px.shape stat = GMMStats(dim_c, dim_d) stat.t = long(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.ubm = 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 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 = gmm_from_data(group['template_model'].data), ) # process the probe comparison_ids = inputs['comparison_ids'].data.text statistics = stats_from_data(inputs['probe_statistics'].data) scores = [] for comparison_id in comparison_ids: template_client_identity = self.templates[comparison_id]['client_id'] score = bob.machine.linear_scoring([self.templates[comparison_id]['model']], self.ubm, [statistics])[0,0] scores.append({ 'template_identity': template_client_identity, 'score': score, }) outputs['scores'].write({ 'client_identity': inputs['probe_client_id'].data.text, 'scores': scores }, end_data_index=inputs['probe_id'].data_index_end ) return TrueThe code for this algorithm in Python
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For a given set of feature vectors, a Gaussian Mixture Model (GMM) of the target client and an UBM-GMM, this algorithm computes the scoring using the linear scoring implemented on the `Bob https://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/machine/generated/bob.machine.linear_scoring.html?highlight=linear%20scoring#bob.machine.linear_scoring`_
This algorithm relies on the Bob library.