Abstract

The generation of cryptographic keys from biometric traits offers a secure alternative to password-based authentication, but is hindered by challenges related to entropy, reproducibility, and adversarial resistance. This work presents a dual-path framework in which a Continuous Thinking Machine Model (CTMM) extracts multimodal embeddings from iris and fingerprint data. Feature vectors undergo projection through principal component analysis and graph-based distance encoding, followed by chaotic sequence modeling with Lorenz-like dynamics and an error-correcting routine to stabilize bitstreams. A secure mixing function consolidates the outputs, while SHA3-512 ensures deterministic expansion. Final passkeys are generated using the Kyber512 post-quantum key encapsulation mechanism (KEM), with neuro-symbolic reasoning applied as a validation layer to enforce entropy, avalanche properties, and inter-user separation. Evaluation confirmed compliance with NIST statistical tests, including monobit, runs, and longest-run assessments, while the system maintained a near-zero false acceptance rate. The originality of this work lies in combining CTMM-driven multimodal feature extraction with a quantum-safe cryptographic pipeline, augmented by neuro-symbolic validation, to establish a reproducible and secure method for biometric passkey generation in high-assurance authentication contexts.