Benchmarking of Cancelable Biometrics for Deep Templates
This work provides a comparative analysis for researchers and practitioners in biometric security, though it is incremental as it benchmarks existing methods on new data.
The paper benchmarks several cancelable biometrics schemes, including BioHashing and IoM-based methods, on deep templates from face, voice, finger vein, and iris, evaluating unlinkability, irreversibility, and recognition performance as per ISO/IEC 24745 standards, with results showing varying performance across schemes and characteristics.
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.