BEAT - pkorshunov/lr-spoofing-score-fusion-train/2

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

Endpoint Groups 3

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.

Group: asv_real

Endpoint Name	Data Format	Nature
asv_real_scores	elie_khoury/string_probe_scores/1	Input
asv_probe_id	system/text/1	Input
classifier	tutorial/linear_machine/1	Output

Group: pad

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

Group: asv_attack

Endpoint Name	Data Format	Nature
asv_attack_scores	elie_khoury/string_probe_scores/1	Input
asv_attack_id	system/text/1	Input

import bob
import bob.learn.linear
import numpy
import math

def zeromean_unitvar_norm(data, mean, std):
    """ Normalize the data with zero mean and unit variance. Mean and variance are in numpy.ndarray format"""
    return numpy.divide(data-mean,std)

class Algorithm:

def __init__(self):
        self.all_pad_scores = []  # scores from pad
        self.all_asv_scores = []  # scores form asv
        self.train_real = []
        self.train_zimp = []
        self.train_attack = None
        self.pad_real_id_matched = None
        self.pad_attack_id_matched = None

def process(self, inputs, outputs):

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 = {}
            # accumulates the scores from PAD
            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
                self.all_pad_scores.append(score_pad)  # add score to the array with all

if self.train_attack is None:
            self.train_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.train_attack.append((score.score, corresponding_pad_attack_score))
                        self.all_asv_scores.append(score.score)

# accumulate the scores from ASV
        asv_probe_id = inputs['asv_probe_id'].data.text
        print ("asv_probe_id: %s" % asv_probe_id)
        corresponding_pad_real_score = self.pad_real_id_matched[asv_probe_id]
        data_asv = inputs['asv_real_scores'].data
        for score in data_asv.scores:
            # if this is real score
            if score.template_identity == data_asv.client_identity:
                self.train_real.append((score.score, corresponding_pad_real_score))
            # if this is zero-impostor score
            else:
                self.train_zimp.append((score.score, corresponding_pad_real_score))
            self.all_asv_scores.append(score.score)

if not(inputs.hasMoreData()):
            self.all_pad_scores = numpy.asarray(self.all_pad_scores, dtype=numpy.float64)
            self.all_asv_scores = numpy.asarray(self.all_asv_scores, dtype=numpy.float64)

# compute a combined (asv,pad) mean and std
            pad_mean =  numpy.average(self.all_pad_scores, axis=0)
            pad_std  = numpy.std(self.all_pad_scores, axis=0)
            asv_mean =  numpy.average(self.all_asv_scores, axis=0)
            asv_std  = numpy.std(self.all_asv_scores, axis=0)

scores_mean = numpy.asarray([asv_mean, pad_mean], dtype=numpy.float64)
            print ("scores_mean ", scores_mean)
            scores_std = numpy.asarray([asv_std, pad_std], dtype=numpy.float64)
            print ("scores_std ", scores_std)

# put together and normalize real scores from pad and asv
            self.train_real = zeromean_unitvar_norm(self.train_real, scores_mean, scores_std)
            # put together and normalize zimp scores from pad and asv
            self.train_zimp = zeromean_unitvar_norm(self.train_zimp, scores_mean, scores_std)
            # put together and normalize attack scores from pad and asv
            self.train_attack = zeromean_unitvar_norm(self.train_attack, scores_mean, scores_std)

print("self.train_real len: %d, self.train_zimp len: %d, self.train_attack len: %d" %(len(self.train_real), len(self.train_zimp), len(self.train_attack)))
            
            # trains the Logistic Regression classifier
            positives = self.train_real
            negatives = numpy.concatenate((self.train_zimp, self.train_attack), axis=0)

trainer = bob.learn.linear.CGLogRegTrainer()
            machine = trainer.train(negatives, positives)
            if scores_mean is not None and scores_std is not None:
                machine.input_subtract = scores_mean
                machine.input_divide = scores_std

# outputs data
            outputs["classifier"].write({
                'input_subtract': machine.input_subtract,
                'input_divide':   machine.input_divide,
                'weights':        machine.weights,
                'biases':         machine.biases,
            })

return True

The code for this algorithm in Python
The ruler at 80 columns indicate suggested POSIX line breaks (for readability).
The editor will automatically enlarge to accomodate the entirety of your input
Use keyboard shortcuts for search/replace and faster editing. For example, use Ctrl-F (PC) or Cmd-F (Mac) to search through this box

This algorithm will run a Logistic Regression model [LR] for a binary classification problem using features as inputs.

The inputs take feature vectors as input and a text flag indicating if the data is a hit (it should be &amp;amp;amp;#39;real&amp;amp;amp;#39;) or a miss.

[LR]	https://en.wikipedia.org/wiki/Logistic_regression

Experiments

Attestation:

Privacy:

Status:

			Updated	Name	Databases/Protocols	Analyzers
				pkorshunov/pkorshunov/isv-asv-pad-fusion-complete/1/asv_isv-pad_gmm-fusion_lr-pa	avspoof/2@physicalaccess_verification,avspoof/2@physicalaccess_verification_spoof,avspoof/2@physicalaccess_antispoofing,avspoof/2@physicalaccess_verify_train_spoof,avspoof/2@physicalaccess_verify_train	pkorshunov/spoof-score-fusion-roc_hist/1

Scientific Python 2.7 (0.1.0) 5

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.

algorithms pkorshunov lr-spoofing-score-fusion-train 2

Endpoint Groups 3

Group: asv_real

Group: pad

Group: asv_attack

Experiments

algorithms

pkorshunov

lr-spoofing-score-fusion-train

2