On the Statistical Independence of Parametric Representations in Biometric Cryptosystems: Evaluation and Improvement

Riccardo Musto, Emanuele Maiorana, Ridvan Kuzu, Gabriel Hine, Patrizio Campisi

2022

Abstract

Biometric recognition is nowadays employed in several real-world applications to automatically authenticate legitimate users. Nonetheless, using biometric traits as personal identifiers raises many privacy and security issues, not affecting traditional approaches performing automatic people recognition. In order to cope with such concerns, and to guarantee the required level of security to the employed biometric templates, several protection schemes have been designed and proposed. The robustness against possible attacks brought to such approaches has been typically investigated under the assumption that the employed biometric representations comprise mutually independent coefficients. Unfortunately, the parametric representations adopted in most biometric recognition systems commonly consist of strongly correlated features, which may be therefore unsuitable to be used in biometric cryptosystems since they would lower the achievable security. In this paper we propose a framework for evaluating the statistical independence of features employed in biometric recognition systems. Furthermore, we investigate the feasibility of improving the mutual independence of representations defined through deep learning approaches by resorting to architectures involving autoencoders, and evaluate the characteristics of the novel templates through the introduced metrics. Tests performed using templates derived from finger-vein patterns are performed to evaluate the introduced framework for statistical independence and the proposed template generation strategies.

Paper Citation

in Harvard Style

Musto R., Maiorana E., Kuzu R., Hine G. and Campisi P. (2022). On the Statistical Independence of Parametric Representations in Biometric Cryptosystems: Evaluation and Improvement. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 480-487. DOI: 10.5220/0010988300003122

in Bibtex Style

@conference{icpram22,
author={Riccardo Musto and Emanuele Maiorana and Ridvan Kuzu and Gabriel Hine and Patrizio Campisi},
title={On the Statistical Independence of Parametric Representations in Biometric Cryptosystems: Evaluation and Improvement},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={480-487},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010988300003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - On the Statistical Independence of Parametric Representations in Biometric Cryptosystems: Evaluation and Improvement
SN - 978-989-758-549-4
AU - Musto R.
AU - Maiorana E.
AU - Kuzu R.
AU - Hine G.
AU - Campisi P.
PY - 2022
SP - 480
EP - 487
DO - 10.5220/0010988300003122