ECC-Driven Lightweight Iris Recognition for Blockchain and IoT Ecosystems

Managing limited computational resources while implementing efficient biometric authentication is a significant challenge in today’s blockchain and IoT security landscape. To address this, We developed a new iris authentication system that uses advanced template transformation methods combined with Elliptic Curve Cryptography (ECC) for better security. The system operates in multiple phases: it begins by processing raw iris images to generate targeted, segmented templates that capture essential information. These templates are then transformed using ECC, chosen for its optimal balance of computational efficiency and security strength. By extracting key properties from these encrypted templates and storing them in a secure database, the solution provides robust protection against unauthorized access and modification. This architecture is specifically designed to meet the unique requirements of blockchain and IoT environments, where reliable and resource-efficient security solutions are crucial. Our experimental results demonstrate that system achieves exceptional performance testing on two distinct datasets yielded Equal Error Rate (EER) of 15.25% and 7.51%, respectively. While these results indicate significant progress, they also highlight areas for improvement, particularly in managing dataset variations and enhancing recognition precision. Overall, the architecture offers effective security measures against potential cyber-attacks in blockchain and IoT deployments and shows great promise for protecting biometric information in resource-constrained systems.