The Cancelable Multimodal Template Protection Algorithm Based on Random Index

Abstract

Current multimodal template protection methods typically require encryption or transformation of the original biometric features. However, these operations carry certain risks, as attackers may reverse-engineer or decrypt the protected multimodal templates to retrieve partial or complete information about the original templates, leading to the leakage of the original biometric features. To address this issue, we propose a cancelable multimodal template protection method based on random indexing. First, hash functions are used to generate integer sequences as index values, which are then employed to create single-modal cancelable templates using random binary vectors. Second, the single-modal cancelable templates are used as indices for random binary sequences, which locate the corresponding template information and are filled into the fusion cancelable template at the respective positions, achieving template fusion. The resulting template is unrelated to the original biometric features. Finally, without directly storing the binary factor sequences, an XOR operation is performed on the extended biometric feature vectors and random binary sequences to generate the encoded key. Experimental results demonstrate that the proposed method significantly enhances performance on the FVC2002DB1 fingerprint, MMCBNU_6000 finger-vein, and NUPT_FPV databases, while also satisfying the standards for cancelable biometric feature design. We also analyze four privacy and security attacks against this scheme.