Cancelable Iris Recognition Scheme Based on Positive–Negative Mapping and Negative Selection

Abstract

Iris recognition technology is widely used in identity verification and other fields due to its high accuracy and strong security. However, with the popularization of the technology, the privacy protection of iris data has become increasingly prominent. Existing systems are often difficult to protect personal data privacy while guaranteeing recognition accuracy, especially when facing data leakage and attacks, iris features cannot be modified once leaked, which is extremely risky. Therefore, proposing an efficient and accurate protection scheme that can ensure the security of iris data has become an important issue that needs to be solved urgently. In this paper, we propose an iris template protection framework based on positive-negative mapping and the negative selective algorithm, called the IrisPN-NS. The framework effectively protects iris data by constructing a complex system of inequality equations and combining it with the negative selection algorithm, thus increasing the difficulty for attackers to crack. Based on this framework, we propose two iris template protection methods, IrisPNM-NS and IrisPNS-NS. We conducted experiments on the CASIA-IrisV3-Interval, CASIA-IrisV4-Lamp, and IIT Delhi Iris datasets. The experimental results show that the scheme achieves high performance and effectively resists the attack of the greedy algorithm, while satisfying the requirements of irreversibility, revocability, and unlinkability.