Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/4051 2018 2018 Informatics Institute for Postgraduate Studies, Information Technology & Communications University, Baghdad, Iraq Nahla Nahla Department of Computer Science, Faculty of Sciences, Mustansiriyah University, Baghdad, Iraq Bashar M. Nema 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. 2026 2026 119 148 10.54216/FPA.210208 https://www.americaspg.com/articleinfo/3/show/4051