Co-Learning to Hash Palm Biometrics for Flexible IoT Deployment

Abstract

Security enhancement via trustworthy identity authentication in Internet of Things (IoT) has soared recently. Biometrics offers a promising remedy to improve the security and utility of IoT and play a role in securing a variety of low-power and limited computing capability IoT devices to address identity management challenges. This article proposes an IoT-compliant co-learned biometric hashing network derived from palm print and palm vein dubbed PalmCohashNet. The PalmCohashNet comprises two hashing subnetworks, one for each palm modality, and is trained collaboratively to generate shared hash codes for respective modality (co-hash codes). A cross-modality hashing (CMH) loss is devised to encourage co-hash codes of palm vein and palm print from the same identity to be adjacent and consistent; meanwhile, pull the co-hash codes of each identity to a preassigned identity-specific hash centroid that is shared by both palm modalities. Two palm-based co-hash codes of a person can be generated simultaneously for deployment. The binary co-hash code is IoT compliant attributed to its highly compact form for storage and fast matching. A trained PalmCohashNet can be flexibly deployed under four operation modes: single-modality matching (print versus print or vein versus vein), multimodality matching where both print and vein are utilized, and cross-modality matching (print versus vein) depending on the IoT service context. Our empirical results on four publicly available palm databases show that the proposed method consistently outperforms state-of-the-art methods.