  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.
