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:在本文中,我们根据不同的生物鉴别特征对若干可取消的生物鉴别方法(CB)进行基准评估。我们考虑了生物鉴别、多功能受体(MLP)散射、闪光过滤器和基于Meximum指数(IOM)散射的两个方案(即IOM-URP和IOM-GRP),除了上述的CB计划之外,我们还根据用户特有的随机转换和二元化,引入了CB计划(作为基准 ) 。我们评估了这些CB计划的不可连接性、不可逆转性和识别性(这是ISO/IEC 24745标准所要求的标准 ), 其所依据的是基于不同生理和行为生物鉴别特征的深层学习模板, 包括面部、声音、手指和Iris。此外,我们提供了所有实验的公开源实施,以促进我们结果的再生。</s>