Learning Biometric Representations with Mutually Independent Features Using Convolutional Autoencoders

Representations of biometric traits to be used in automatic recognition systems are typically learned with the goal of obtaining significant discriminative capabilities, that is, generating features that are notably different when produced by traits of different subjects, while maintaining an approp...

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Veröffentlicht in:SN computer science Jg. 4; H. 5; S. 619
Hauptverfasser: Musto, Riccardo, Kuzu, Ridvan Salih, Maiorana, Emanuele, Hine, Gabriel Emile, Campisi, Patrizio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.09.2023
Springer Nature B.V
Schlagworte:
Access control
Advances on Pattern Recognition Applications and Methods 2022
Biometric recognition systems
Biometrics
Coefficients
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data integrity
Data Structures and Information Theory
Fingers
Information Systems and Communication Service
Learning
Original Research
Pattern Recognition and Graphics
Performance evaluation
Representations
Software Engineering/Programming and Operating Systems
Veins
Vision
Representation learning
Biometric recognition
Statistical independence
Vein patterns
ISSN:2661-8907, 2662-995X, 2661-8907
Online-Zugang:Volltext
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Zusammenfassung:Representations of biometric traits to be used in automatic recognition systems are typically learned with the goal of obtaining significant discriminative capabilities, that is, generating features that are notably different when produced by traits of different subjects, while maintaining an appropriate consistency for a given user. Nonetheless, discriminability is not the only desirable property of a biometric representation. For instance, the mutual independence of the coefficients in the employed templates is a valuable property when designing biometric template protection schemes. In fact, managing representations with independent coefficients allows to maximize the achievable security. In this paper we propose different learning strategies to obtain biometric representations with the property of statistical independence among coefficients, while preserving discriminability. In order to achieve this goal, different strategies are employed to train convolutional autoencoders. As a proof of concept, the effectiveness of the proposed approaches is tested by considering biometric recognition systems using both finger-vein and palm-vein patterns.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-01974-z