GLU-Attention Hybrid Architecture for Dual-Biometric Passkey Generation via Neuro-Symbolic and Chaotic Dynamics

Nahla Abdulnabee Sameer^1,*, Bashar M. Nema²

¹Informatics Institute for Postgraduate Studies, Information Technology & Communications University, Baghdad, Iraq

²Department of Computer Science, Faculty of Sciences, Mustansiriyah University, Baghdad, Iraq

Emails: nahlaphd1973@gmail.com; bashar_sh77@uomustansiriyah.edu.iq

Abstract

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.

Keywords: Biometric cryptography; Gated Linear Units (GLU); Squeeze-and-Excitation (SE); Cryptographic key generation; NIST randomness tests; Secure passkey