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 paper proposes an IoT-compliant co-learned biometric hashing network derived from palm print and palm vein dubbed PalmCohashNet. The PalmCohashNet comprises two hashing sub-networks, one for each palm modality, and is trained collaboratively to generate shared hash codes for respective modality (co-hash codes). A cross-modality hashing 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 pre-assigned 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 vs. print or vein vs. vein), multi-modality matching where both print and vein are utilized, and cross-modality matching (print vs. 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.
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications