Abstract
The proliferation of biometric technology across various domains including user identification,
financial services, healthcare, security, law enforcement, and border control introduces
convenience in user identity verification while necessitating robust protection mechanisms
for sensitive biometric data. While chaos-based encryption systems offer promising solutions,
many existing chaos-based encryption schemes exhibit inherent shortcomings
including deterministic randomness and constrained key spaces, often failing to balance
security robustness with computational efficiency. To address this, we propose a novel duallayer
cryptographic framework leveraging a four-dimensional (4D) Qi hyperchaotic system
for protecting biometric templates and facilitating secure feature matching operations. The
framework implements a two-tier encryption mechanism where each layer independently
utilizes a Qi hyperchaotic system to generate unique encryption parameters, ensuring
template-specific encryption patterns that enhance resistance against chosen-plaintext attacks.
The framework performs dimensional normalization of input biometric templates,
followed by image pixel shuffling to permutate pixel positions before applying dual-key
encryption using the Qi hyperchaotic system and XOR diffusion operations. Templates
remain encrypted in storage, with decryption occurring only during authentication processes,
ensuring continuous security while enabling biometric verification. The proposed
system’s framework demonstrates exceptional randomness properties, validated through
comprehensive NIST Statistical Test Suite analysis, achieving statistical significance across
all 15 tests with p-values consistently above 0.01 threshold. Comprehensive security analysis
reveals outstanding metrics: entropy values exceeding 7.99 bits, a key space of 10320,
negligible correlation coefficients (<10−2), and robust differential attack resistance with
an NPCR of 99.60% and a UACI of 33.45%. Empirical evaluation, on standard CASIA
Face and Iris databases, demonstrates practical computational efficiency, achieving average
encryption times of 0.50913s per user template for 256 × 256 images. Comparative analysis
against other state-of-the-art encryption schemes verifies the effectiveness and reliability
of the proposed scheme and demonstrates our framework’s superior performance in both
security metrics and computational efficiency. Our findings contribute to the advancement
of biometric template protection methodologies, offering a balanced performance between
security robustness and operational efficiency required in real-world deployment scenarios.