Benchmarking of cancelable biometrics for deep templates

Abstract Biometrics has become a viable and popular solution for applications which require secure authentication. In spite of the advantages of biometrics as an automatic authentication technology, the usage of biometric characteristics raises significant concerns regarding personal data and privacy of subjects in these systems. To address these concerns, several biometric template protection schemes have been proposed in the literature to achieve trustworthy, reliable and privacy-preserving systems. 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 behavioural biometric characteristics including face, voice, finger vein, and iris. Our experiments show that all the studied CB schemes are almost unlinkable for different characteristics. We also observe that the mutual information (MI) between protected and unprotected templates varies according to the scenario and biometric characteristic. In terms of recognition accuracy, our study shows that deep templates protected by Bloom filters suffer from a drop in performance, while other CB schemes achieve competitive accuracies for different biometric characteristics. We provide an open-source implementation of all the experiments presented to facilitate the reproducibility of our results: https://github.com/otroshi/benchmark_cb .