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.

翻译：本文对不同生物特征模态下的多种可撤销生物特征（CB）方案进行了基准测试。我们考虑了BioHashing、多层感知器（MLP）哈希、布隆过滤器以及两种基于最大索引哈希的方案（即IoM-URP和IoM-GRP）。除上述CB方案外，我们还引入了一种基于用户特定随机变换后进行二值化的CB方案（作为基线）。我们从不可关联性、不可逆性和识别性能（这是ISO/IEC 24745标准所要求的标准）三个方面，对这些CB方案在基于深度学习提取的模板上进行了评估，这些模板来自包括人脸、语音、手指静脉和虹膜在内的不同生理和行为生物特征。此外，我们提供了所有实验的开源实现，以促进我们结果的可重复性。