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

Endpoint Groups 2

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: probes

Endpoint Name	Data Format	Nature
comparison_ids	system/array_1d_text/1	Input
keystroke	tutorial/atvs_keystroke/1	Input
probe_client_id	system/text/1	Input
scores	elie_khoury/string_probe_scores/1	Output

Group: templates

Endpoint Name	Data Format	Nature
template_client_id	system/text/1	Input
template_id	system/text/1	Input
model_template	aythamimm/keystroke_model/6	Input

Parameters 1

Parameters allow users to change the configuration of an algorithm when scheduling an experiment

Name	Description	Type	Default	Range/Choices
field		string	given_name	given_name, family_name, email, nationality, id_number, all_five

import numpy

def scaled_distance(in1,in2,in3):    
    dif=[]
    mean_gal_features=in1
    std_gal_features=in2
    probe_features=in3
    l_features=len(probe_features)
       
    std_threshold=0.2*numpy.mean(std_gal_features);
    for i in range( l_features):
        if std_gal_features[i] < std_threshold:
            std_gal_features[i]=std_threshold    
    
    for i_dif in range(l_features):
        dif.append((abs(mean_gal_features[i_dif]-probe_features[i_dif]))/std_gal_features[i_dif])            
    score = 1/numpy.average(dif)    
    return score

class Algorithm:

def __init__(self):        
        self.templates = None   
        self.field='given_name'
	
    def setup(self, parameters):         
        self.field = parameters.get('field', self.field)
        
        return True
    
    def process(self, inputs, outputs):

# retrieve all the templates once
        if self.templates is None:
            self.templates = {}
            group = inputs.groupOf('model_template')

while group.hasMoreData():
                group.next()

template_id = group['template_id'].data.text                
                
                if self.field == 'given_name':
                	template1=group['model_template'].data.given_name_average
                	template2=group['model_template'].data.given_name_std
                if self.field == 'family_name':
                    template1=group['model_template'].data.family_name_average
                    template2=group['model_template'].data.family_name_std
                if self.field == 'email':
                    template1=group['model_template'].data.email_average
                    template2=group['model_template'].data.email_std
                if self.field == 'nationality':
                    template1=group['model_template'].data.nationality_average
                    template2=group['model_template'].data.nationality_std
                if self.field == 'id_number':
                    template1=group['model_template'].data.id_number_average
                    template2=group['model_template'].data.id_number_std
                if self.field == 'all_five':
                    template11=group['model_template'].data.given_name_average
                    template12=group['model_template'].data.given_name_std                
                    template21=group['model_template'].data.family_name_average
                    template22=group['model_template'].data.family_name_std              
                    template31=group['model_template'].data.email_average
                    template32=group['model_template'].data.email_std                
                    template41=group['model_template'].data.nationality_average
                    template42=group['model_template'].data.nationality_std
                    template51=group['model_template'].data.id_number_average
                    template52=group['model_template'].data.id_number_std 
                
                if self.field != 'all_five':
                	self.templates[template_id] = dict(
                    	client_id = group['template_client_id'].data.text,                    
                    	model_average = template1,
                    	model_std = template2,                      
                	)
                if self.field == 'all_five':
                	self.templates[template_id] = dict(
                    	client_id = group['template_client_id'].data.text,                    
                    	model_average1 = template11,
                    	model_std1 = template12,
                        model_average2 = template21,
                    	model_std2 = template22,
                        model_average3 = template31,
                    	model_std3 = template32,
                        model_average4 = template41,
                    	model_std4 = template42,
                        model_average5 = template51,
                    	model_std5 = template52,
                	)

# process the probe
        comparison_ids = inputs['comparison_ids'].data.text
        data = inputs['keystroke'].data		
        if self.field == 'given_name':
        	f1 = data.holdtime.given_name
        	f2 = data.rplatency.given_name
        if self.field == 'family_name':
        	f1 = data.holdtime.family_name
        	f2 = data.rplatency.family_name
        if self.field == 'email':
        	f1 = data.holdtime.email
        	f2 = data.rplatency.email
        if self.field == 'nationality':
        	f1 = data.holdtime.nationality
        	f2 = data.rplatency.nationality        
        if self.field == 'id_number':
        	f1 = data.holdtime.id_number
        	f2 = data.rplatency.id_number
        if self.field == 'all_five':
        	f11 = data.holdtime.given_name
        	f12 = data.rplatency.given_name        
        	f21 = data.holdtime.family_name
        	f22 = data.rplatency.family_name      
        	f31 = data.holdtime.email
        	f32 = data.rplatency.email       
        	f41 = data.holdtime.nationality
        	f42 = data.rplatency.nationality 
        	f51 = data.holdtime.id_number
        	f52 = data.rplatency.id_number 
        
        if self.field != 'all_five':
            feature_vector=[]
            l_f=len(f1)
            for i_k in range(l_f):
                feature_vector.append(float(f1[i_k]))
            
            l_f=len(f2)
            for i_k in range(l_f):
                feature_vector.append(float(f2[i_k]))        
        	
            probe_features=feature_vector
            
        if self.field == 'all_five':
            feature_vector1=[]
            l_f=len(f11)
            for i_k in range(l_f):
                feature_vector1.append(float(f11[i_k]))
        
        	l_f=len(f12)
            for i_k in range(l_f):
                feature_vector1.append(float(f12[i_k]))        
        	
            feature_vector2=[]
            l_f=len(f21)
            for i_k in range(l_f):
                feature_vector2.append(float(f21[i_k]))
        
        	l_f=len(f22)
            for i_k in range(l_f):
                feature_vector2.append(float(f22[i_k]))  
            
            feature_vector3=[]
            l_f=len(f31)
            for i_k in range(l_f):
                feature_vector3.append(float(f31[i_k]))
        
        	l_f=len(f32)
            for i_k in range(l_f):
                feature_vector3.append(float(f32[i_k]))  
            
            feature_vector4=[]
            l_f=len(f41)
            for i_k in range(l_f):
                feature_vector4.append(float(f41[i_k]))
        
        	l_f=len(f42)
            for i_k in range(l_f):
                feature_vector4.append(float(f42[i_k]))  
            
        	feature_vector5=[]
            l_f=len(f51)
            for i_k in range(l_f):
                feature_vector5.append(float(f51[i_k]))
        
        	l_f=len(f52)
            for i_k in range(l_f):
                feature_vector5.append(float(f52[i_k])) 
        
        	probe_features1=feature_vector1
            probe_features2=feature_vector2
            probe_features3=feature_vector3
            probe_features4=feature_vector4
            probe_features5=feature_vector5
        
        scores = []
        for comparison_id in comparison_ids:
            template_client_identity = self.templates[comparison_id]['client_id']            
            
            if self.field != 'all_five':
                score=scaled_distance(self.templates[comparison_id]['model_average'],self.templates[comparison_id]['model_std'],probe_features);
                
            if self.field == 'all_five':
                score=[]
                score.append(scaled_distance(self.templates[comparison_id]['model_average1'],self.templates[comparison_id]['model_std1'],probe_features1))
                score.append(scaled_distance(self.templates[comparison_id]['model_average2'],self.templates[comparison_id]['model_std2'],probe_features2))
                score.append(scaled_distance(self.templates[comparison_id]['model_average3'],self.templates[comparison_id]['model_std3'],probe_features3))
                score.append(scaled_distance(self.templates[comparison_id]['model_average4'],self.templates[comparison_id]['model_std4'],probe_features4))
                score.append(scaled_distance(self.templates[comparison_id]['model_average5'],self.templates[comparison_id]['model_std5'],probe_features5))
                score=numpy.average(score)
                
            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_client_id'].data_index_end
        )

return True

The 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`_

Docutils System Messages

System Message: ERROR/3 (<string>, line 1); backlink

Unknown target name: "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".

System Message: ERROR/3 (<string>, line 3); backlink

Unknown target name: "bob &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;lt;http://www.idiap.ch/software/bob/&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;".

Experiments

Attestation:

Privacy:

Status:

			Updated	Name	Databases/Protocols	Analyzers
				aythamimm/aythamimm/btas15_keystroke_experiments/6/BTAS_2015_Kesytroke_Experiment	atvskeystroke/1@A	aythamimm/analyzer_keystroke/70

Scientific Python 2.7 (0.0.3) 8

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 aythamimm manhattan_scaled_scoring 63