Abstract
Private keys are the single point of failure in blockchain systems; if lost, assets are irrecoverable, and if stored, they are exposed. This paper presents a template-free biometric cryptosystem that regenerates a 256-bit private key directly from a fingerprint, storing neither the biometric template nor the key. A Siamese convolutional network trained with a two-stage composite loss produces a 1024-bit binary representation; an adaptive, margin-based binarisation with stability-aware selection retains 288 reliable bits; and a block-wise BCH fuzzy extractor regenerates the exact key, while a layered scheme of Scrypt, AES-256-GCM, and Shamir secret sharing protects the helper data. Evaluated on the PolyU fingerprint dataset under a strict cross-session protocol, the system reduces the mean intra-user bit error rate from 8.13% to 2.35%, attains an equal-error rate of 6.27% and an area under the curve of 0.9831, and achieves a 95.83% key-reconstruction success rate with a 0% false key-acceptance rate. The 288-bit key has an estimated effective min-entropy of 269 bits, and helper-data leakage is negligible (mutual information 0.0132). The results show that template-free, cross-session key generation is feasible at the 256-bit level required for blockchain.