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Benchmarking of Cancelable Biometrics for Deep Templates

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arxiv 2302.13286 v1 pith:DEXFD7VH submitted 2023-02-26 cs.CV cs.CR

Hatef Otroshi Shahreza , Pietro Melzi , Dailé Osorio-Roig , Christian Rathgeb , Christoph Busch , Sébastien Marcel , Ruben Tolosana , Ruben Vera-Rodriguez This is my paper · ORCID
classification cs.CVcs.CR
keywords schemesadditionbiometricbiometricscancelablecharacteristicsdeepdifferent
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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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