Benchmarking of Cancelable Biometrics for Deep Templates

• URL: http://arxiv.org/abs/2302.13286v1
• Date: Sun, 26 Feb 2023 10:35:45 GMT
• Title: Benchmarking of Cancelable Biometrics for Deep Templates
• Authors: Hatef Otroshi Shahreza, Pietro Melzi, Dail\'e Osorio-Roig, Christian Rathgeb, Christoph Busch, S\'ebastien Marcel, Ruben Tolosana, Ruben Vera-Rodriguez
• Abstract summary: We benchmark several cancelable biometrics schemes on different biometric characteristics. We consider BioHashing, Multi-Layer Perceptron (MLP) Hashing, Bloom Filters, and two schemes based on Index-of-Maximum (IoM) Hashing. In addition, we introduce a CB scheme based on user-specific random transformations followed by binarization.
• Score: 12.803168058051542
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we benchmark several cancelable biometrics (CB) schemes on different biometric characteristics. We consider BioHashing, Multi-Layer Perceptron (MLP) Hashing, Bloom Filters, and two schemes based on Index-of-Maximum (IoM) Hashing (i.e., IoM-URP and IoM-GRP). In addition to the mentioned CB schemes, we introduce a CB scheme (as a baseline) based on user-specific random transformations followed by binarization. We evaluate the unlinkability, irreversibility, and recognition performance (which are the required criteria by the ISO/IEC 24745 standard) of these CB schemes on deep learning based templates extracted from different physiological and behavioral biometric characteristics including face, voice, finger vein, and iris. In addition, we provide an open-source implementation of all the experiments presented to facilitate the reproducibility of our results.

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