A Hybrid Chaotic and Random Grid Visual Cryptography-Based Framework for Secure and Revocable Biometric Template Protection

Biometric authentication systems are increasingly deployed in critical security applications, yet the irreplaceable nature of biometric traits poses fundamental risks when templates are compromised. Unlike passwords or tokens, biometric data cannot be reissued, making template protection a paramount concern for preserving both security and privacy. This paper presents a novel multilayer framework for biometric template protection that integrates cancellability directly into the feature extraction stage, ensuring non-invertible and revocable templates while maintaining high recognition accuracy. The proposed method employs chaotic projection of Binarized Statistical Image Features (BSIF) filter banks, optimized through Particle Swarm Optimization (PSO), to generate discriminative yet irreversible biometric templates. To strengthen security against statistical and cryptanalytic attacks, dual-layer scrambling and diffusion processes driven by chaotic maps eliminate spatial correlations and produce uniform intensity distributions. Furthermore, Random Grid Visual Cryptography (RGVC) divides the encrypted template into two shares stored in separate databases, ensuring that the compromise of a single repository reveals no biometric information. Extensive experiments conducted on the PolyU multispectral palmprint database demonstrate exceptional authentication performance, achieving Equal Error Rate (EER) values as low as 0.0520% after applying the proposed protection framework, under optimal configurations. Comprehensive empirical security analysis demonstrates favorable statistical security characteristics, including near-zero pixel correlation, near-uniform intensity distributions, high entropy values approaching the theoretical maximum of 8 bits, favorable NPCR and UACI values, and high sensitivity to key variations under the considered experimental settings. The proposed framework satisfies the essential requirements of cancellable biometrics, including diversity, revocability, and non-invertibility, while providing a privacy-preserving biometric template protection approach.