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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 |
|---|---|---|
| scores_test | system/array_1d_floats/1 | Input |
| label_test | system/text/1 | Input |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| scores_dev | system/array_1d_floats/1 | Input |
| label_dev | system/text/1 | Input |
Analyzers may produce any number of results. Once experiments using this analyzer are done, you may display the results or filter experiments using criteria based on them.
| Name | Type |
|---|---|
| test_frr | float32 |
| dev_eer | float32 |
| dev_roc | plot/isoroc/1 |
| test_roc | plot/isoroc/1 |
| test_numNegatives | int32 |
| dev_numNegatives | int32 |
| dev_scoreDistribution | plot/bar/1 |
| test_numPositives | int32 |
| dev_far | float32 |
| test_hter | float32 |
| dev_numPositives | int32 |
| test_scoreDistribution | plot/bar/1 |
| test_far | float32 |
| dev_frr | float32 |
| dev_eer_threshold | float32 |
xxxxxxxxxximport numpyimport bob.measure# same functionality as earlier version but using Bob_v2class Algorithm: def __init__(self): self.positives_dev = None self.negatives_dev = None self.positives_test = [] self.negatives_test = [] self.n_bins = 50 def process(self, inputs, outputs): # accumulate the dev scores if self.positives_dev is None: self.positives_dev = [] self.negatives_dev = [] group = inputs.groupOf('scores_dev') while group.hasMoreData(): group.next() data_dev = inputs['scores_dev'].data.value label_dev = inputs['label_dev'].data.text if label_dev == 'real': self.positives_dev.append(data_dev) else: self.negatives_dev.append(data_dev) # accumulate the test scores data_test = inputs['scores_test'].data.value label_test = inputs['label_test'].data.text if label_test == 'real': self.positives_test.append(data_test) else: self.negatives_test.append(data_test) # once all values are received, evaluate the scores if not(inputs.hasMoreData()): self.positives_dev = numpy.vstack(self.positives_dev).flatten() self.negatives_dev = numpy.vstack(self.negatives_dev).flatten() self.positives_test = numpy.vstack(self.positives_test).flatten() self.negatives_test = numpy.vstack(self.negatives_test).flatten() dev_hist_pos, dev_bin_pos = numpy.histogram(self.positives_dev, self.n_bins) dev_hist_neg, dev_bin_neg = numpy.histogram(self.negatives_dev, self.n_bins) test_hist_pos, test_bin_pos = numpy.histogram(self.positives_test, self.n_bins) test_hist_neg, test_bin_neg = numpy.histogram(self.negatives_test, self.n_bins) eer_threshold = bob.measure.eer_threshold(self.negatives_dev, self.positives_dev) far_dev, frr_dev = bob.measure.farfrr(self.negatives_dev, self.positives_dev, eer_threshold) far_test, frr_test = bob.measure.farfrr(self.negatives_test, self.positives_test, eer_threshold) roc_points_dev = bob.measure.roc(self.negatives_dev, self.positives_dev, 100) roc_points_test = bob.measure.roc(self.negatives_test, self.positives_test, 100) # writes the output back to the platform outputs.write({ 'dev_eer': numpy.float32((far_dev+frr_dev) / 2.), 'test_hter': numpy.float32((far_test+frr_test) / 2.), 'dev_far': numpy.float32(far_dev), 'dev_frr': numpy.float32(frr_dev), 'test_far': numpy.float32(far_test), 'test_frr': numpy.float32(frr_test), 'dev_numPositives': numpy.int32(len(self.positives_dev)), 'dev_numNegatives': numpy.int32(len(self.negatives_dev)), 'test_numPositives': numpy.int32(len(self.positives_test)), 'test_numNegatives': numpy.int32(len(self.negatives_test)), 'dev_eer_threshold': numpy.float32(eer_threshold), 'dev_roc': { "data": [ { "label": "roc_dev", "false_positives": roc_points_dev[1], "false_negatives": roc_points_dev[0], "number_of_positives": numpy.uint64(len(self.positives_dev)), "number_of_negatives": numpy.uint64(len(self.negatives_dev)), } ] }, 'test_roc': { "data": [ { "label": "roc_test", "false_positives": roc_points_test[1], "false_negatives": roc_points_test[0], "number_of_positives": numpy.uint64(len(self.positives_test)), "number_of_negatives": numpy.uint64(len(self.negatives_test)), } ] }, 'dev_scoreDistribution': { "data": [ { "label": "negative scores", "x": dev_bin_neg[:-1], "y": dev_hist_neg, }, { "label": "positive scores", "x": dev_bin_pos[:-1], "y": dev_hist_pos, } ] }, 'test_scoreDistribution': { "data": [ { "label": "negative scores", "x": test_bin_neg[:-1], "y": test_hist_neg, }, { "label": "positive scores", "x": test_bin_pos[:-1], "y": test_hist_pos, } ] }, }) return TrueThe code for this algorithm in Python
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An algorithm that implements standard metrics for antispoofing evaluation.
Specifically, it returns:
This implementation relies on the 'measure' package from the Bob library. See http://www.idiap.ch/software/bob/docs/releases/last/sphinx/html/measure/ for more details.
| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| smarcel/ivana7c/simple-antispoofing-updated/1/replay2-antispoofing-lbp-histograms-fix | replay/3@grandtest | Kanma/iqm_spoof_eer_analyzer/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.