A cancellable iris template protection scheme based on inverse merger and Bloom filter

Abstract

Iris recognition has found extensive applications in real-world situations and financial contexts. However, Iris template protection schemes are highly vulnerable to well-planned attacks that can lead to the leakage of personal information. Once biological information is compromised, this loss is irreversible for the individual. Cancelable protection schemes for iris templates based on the Bloom filter have substantial attention in the field of iris biometrics. Nevertheless, Bloom filter-based template protection schemes face specific security challenges. Therefore, it is crucial to propose a method to protect iris templates that is both secure and efficient. To address irreversible limitations in security analysis, we propose a template protection scheme, a cancelable iris biometric protection scheme based on inverse merger and Bloom filter. The primary idea of the proposed scheme is to perform an inverse merger operation on the acquired codewords before mapping the iris templates to the Bloom filter specifically. Through a comparison of the sizes between the original templates and their inverted counterparts, the template with the smaller size is chosen as the definitive result, subsequently being mapped into the Bloom filter. Our proposed scheme exhibits significant advancements in accuracy across multiple datasets, as evidenced by empirical validations. In the optimal case, our model achieves an excellent performance of 98.04% in terms of GAR, while achieving a significant reduction of 0.51% in terms of EER. Furthermore, a comparative analysis with existing iris template protection methods is performed to evaluate its relative effectiveness in resisting the attack of averaging the columns of a block. The results demonstrate that the scheme exhibits robust resistance to such attacks. The experimental analysis demonstrated that the scheme provided a good balance between accuracy and safety.