Bob 2.0 implementation of feature projection on a linear machine
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 |
|---|---|---|
| asv_real_scores | elie_khoury/string_probe_scores/1 | Input |
| asv_probe_id | system/text/1 | Input |
| fused_scores | pkorshunov/fusion_scores/1 | Output |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| pad_scores_class | system/text/1 | Input |
| pad_file_id | system/text/1 | Input |
| pad_scores | system/float/1 | Input |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| asv_attack_scores | elie_khoury/string_probe_scores/1 | Input |
| asv_attack_id | system/text/1 | Input |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| classifier | tutorial/linear_machine/1 | Input |
xxxxxxxxxximport bob.learn.linearimport numpydef linear_machine_from_data(data): """Extracts a bob.learn.linear.Machine from a BEAT Data object""" machine = bob.learn.linear.Machine(data.weights) machine.biases = data.biases machine.input_subtract = data.input_subtract machine.input_divide = data.input_divide return machineclass Algorithm: def __init__(self): self.machine = None self.toevaluate_real= [] self.toevaluate_zimp = [] self.toevaluate_attack = None self.pad_real_id_matched = None self.pad_attack_id_matched = None def process(self, inputs, outputs): # retrieve the linear machine once if self.machine is None: inputs['classifier'].next() self.machine = linear_machine_from_data(inputs['classifier'].data) # accumulates the scores from PAD if self.pad_real_id_matched is None and self.pad_attack_id_matched is None: self.pad_real_id_matched = {} self.pad_attack_id_matched = {} while inputs['pad_scores'].hasMoreData(): inputs['pad_scores'].next() inputs['pad_scores_class'].next() inputs['pad_file_id'].next() score_pad = inputs['pad_scores'].data.value label_pad = inputs['pad_scores_class'].data.text file_id = inputs['pad_file_id'].data.text if label_pad == 'real': self.pad_real_id_matched[file_id] = score_pad # add real score matching file id else: self.pad_attack_id_matched[file_id] = score_pad # add attack score matching file id if self.toevaluate_attack is None: self.toevaluate_attack = [] while inputs['asv_attack_id'].hasMoreData(): inputs['asv_attack_id'].next() inputs['asv_attack_scores'].next() asv_attack_id = inputs['asv_attack_id'].data.text asv_attack_scores = inputs['asv_attack_scores'].data corresponding_pad_attack_score = self.pad_attack_id_matched[asv_attack_id] for score in asv_attack_scores.scores: if score.template_identity == asv_attack_scores.client_identity: self.toevaluate_attack.append((score.score, corresponding_pad_attack_score)) # get correct scores from ASV real asv_probe_id = inputs['asv_probe_id'].data.text corresponding_pad_real_score = self.pad_real_id_matched[asv_probe_id]# corresponding_pad_real_score = 0.1 asv_real_scores = inputs['asv_real_scores'].data for score in asv_real_scores.scores: # if this is real score if score.template_identity == asv_real_scores.client_identity: self.toevaluate_real.append((score.score, corresponding_pad_real_score)) # if this is zero-impostor score else: self.toevaluate_zimp.append((score.score, corresponding_pad_real_score)) if not(inputs.hasMoreData()): real_scores = [] for score_pair in self.toevaluate_real: real_scores.extend(self.machine(score_pair)) zimp_scores = [] for score_pair in self.toevaluate_zimp: zimp_scores.extend(self.machine(score_pair)) attack_scores = [] for score_pair in self.toevaluate_attack: attack_scores.extend(self.machine(score_pair)) # output the projected scores outputs['fused_scores'].write({ 'real_scores': real_scores, 'zimp_scores': zimp_scores, 'attack_scores': attack_scores }) return TrueThe code for this algorithm in Python
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Project input features on a given linear machine (e.g., logistic regression)
| 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 |
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.