Privacy preserving unique identity generation from multimodal biometric data for privacy and security applications

This study presents a novel approach for generating unique identities from multi-modal biometric data using ensemble feature descriptors extracted from the consistent regions of fingerprint and iris images. The method employs prominent feature selection and discriminant vector generation to enhance intra-class similarity and inter-class separability. Finally, a novel quantization mechanism is used to generate a unique identity. This identity might be vulnerable to many attacks. A shielding mechanism is proposed to address this issue. Experimental results substantiate the method's efficacy, satisfying criteria for distinctiveness, randomness, revocability, and irreversibility. Security analyses showcase resilience against diverse attacks, establishing its applicability in forensic investigations, digital wallets, remote authentication, and other privacy-focused applications. The confidential UID generation scheme ensures privacy and security without involving biometric data or UID enrollment, enhancing its suitability across various applications.