2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 2 , PP: 119-148, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Nahla Abdulnabee Sameer 1 * , Bashar M. Nema 2
Doi: https://doi.org/10.54216/FPA.210208
The generation of cryptographic keys from biometric traits presents an opportunity to replace traditional password-based systems with mechanisms grounded in individual physiology. Nonetheless, reliably deriving secure and reproducible keys from modalities such as fingerprints and irises remains a significant challenge, particularly under varying input conditions and constraints on entropy. In this work, we present a hybrid dual-path deep learning architecture that combines Gated Linear Units (GLUs) with Squeeze-and-Excitation (SE) modules to extract rich, multimodal embeddings from iris and fingerprint images. The model, trained on an augmented cross-modal dataset, achieved a test accuracy of 99.92% and consistently high F1-scores across 50 subjects. To derive the cryptographic key, we apply a multi-stage pipeline that blends principal component projections, distance-based feature encoding, chaotic sequence modeling based on Lorenz-like dynamics, and a lightweight error-correcting routine. These representations are fused via a custom mixing function, producing a 512-bit binary vector subsequently refined using a SHA-256-based HKDF. Evaluation of the generated keys indicates near-ideal entropy, high inter-user separation, and strong avalanche characteristics. The system also passed multiple NIST statistical randomness tests and achieved a near-zero false acceptance rate. These results support the feasibility of the proposed method for secure and repeatable biometric key generation.
Biometric cryptography , Gated Linear Units (GLU) , Squeeze-and-Excitation (SE) , Cryptographic key generation , NIST randomness tests , Secure passkey
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