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1. WO2020136297 - METHOD FOR REMOVING BIAS IN BIOMETRIC RECOGNITION SYSTEMS

Publication Number WO/2020/136297
Publication Date 02.07.2020
International Application No. PCT/ES2019/070877
International Filing Date 23.12.2019
IPC
G06K 9/00 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06K 9/62 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
G06N 3/08 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
CPC
G06F 1/00
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
1Details not covered by groups G06F3/00G06F13/00 and G06F21/00
G06K 9/00885
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
00885Biometric patterns not provided for under G06K9/00006, G06K9/00154, G06K9/00335, G06K9/00362, G06K9/00597; Biometric specific functions not specific to the kind of biometric
G06K 9/6232
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
G06K 9/627
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6267Classification techniques
6268relating to the classification paradigm, e.g. parametric or non-parametric approaches
627based on distances between the pattern to be recognised and training or reference patterns
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
Applicants
  • UNIVERSIDAD AUTÓNOMA DE MADRID [ES]/[ES]
Inventors
  • MORALES MORENO, Aythami
  • ORTEGA GARCÍA, Javier
  • FIERREZ AGUILAR, Julián
  • VERA RODRÍGUEZ, Rubén
Agents
  • PONS ARIÑO, Angel
Priority Data
P20183127824.12.2018ES
Publication Language Spanish (ES)
Filing Language Spanish (ES)
Designated States
Title
(EN) METHOD FOR REMOVING BIAS IN BIOMETRIC RECOGNITION SYSTEMS
(ES) MÉTODO PARA LA ELIMINACIÓN DEL SESGO EN SISTEMAS DE RECONOCIMIENTO BIOMÉTRICO
(FR) PROCÉDÉ POUR L'ÉLIMINATION DE BIAIS DANS DES SYSTÈMES DE RECONNAISSANCE BIOMÉTRIQUE
Abstract
(EN)
The invention relates to a method for removing bias (based on age, ethnicity or gender) in biometric recognition systems, which comprises defining a set of M samples from Y different persons tagged on the basis of attributes such as gender, ethnicity or age, wherein samples A and samples P correspond to samples of the same identity, while samples N correspond to different identities, and wherein a value σ corresponding to the bias of each sample is introduced, the proposed method being characterised in that it comprises the steps necessary to learn a transformation function (see (I)) that generates a new space of characteristics, which allows: (i) the distance d(xA, xP) between characteristic vectors (xA, xP) of A and P to be minimised; (ii) the distance d(xA, xN) between characteristic vectors (xA, xN) of A and N to be maximised; and (iii) bias σ to be reduced in the samples until it is eliminated, thereby ensuring unbiased decision-making.
(ES)
Método para eliminación del sesgo (por edad, etnia o género) en sistemas de reconocimiento biométrico, que comprende definir un conjunto de M muestras de Y personas diferentes etiquetadas a partir de atributos como género, etnia o edad, donde las muestras A y las muestras P se corresponden con muestras de la misma identidad, mientras que las muestras N se corresponden con diferentes identidades y donde, además, se introduce además un valor σ correspondiente con el sesgo de cada muestra; y donde el método propuesto se caracteriza porque comprende las etapas necesarias para aprender una función de transformación (see (I)) que genere un nuevo espacio de características que permita: (i) minimizar distancia d(xA, xP) entre vectores de características (xA, xP) de A y P; (ii) maximizar distancia d(xA, xN) entre vectores de características (xA, xN) A y N; y (iii) reducir sesgo σ en las muestras hasta su eliminación y garantizar así una toma de decisiones no sesgada.
(FR)
L'invention concerne un procédé pour l'élimination du biais (dû à l'âge, l'ethnie, au genre) dans des systèmes de reconnaissance biométrique, qui consiste à définir un ensemble de M échantillons de Y personnes différentes étiquetées à partir d'attributs tels que le genre, l'ethnie ou l'âge, les échantillons A et les échantillons P correspondant avec des échantillons de la même identité, tandis que les échantillons N correspondent avec différentes identités et, en outre, une valeur σ correspondant au biais de chaque échantillon étant introduite; et le procédé selon l'invention étant caractérisé en ce qu'il comprend les étapes nécessaires pour apprendre une fonction de transformation (voir (I)) qui génère un nouvel espace de caractéristiques qui permet de : (i) réduire à un minimum une distance d(xA, xP) entre vecteurs de caractéristiques (xA, xP) de A et P; (ii) maximiser une distance d(xA, xN) entre vecteurs de caractéristiques (xA, xN) A et N; y (iii) réduire le biais σ dans les échantillons jusqu'à son élimination et garantir ainsi une prise de décisions non biaisée.
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