Cancellable Iris Recognition Scheme Based on Inversion Fusion and Local Ranking

Abstract

Iris recognition has gained significant attention and application in real-life and financial scenarios in recent years due to its importance as a biometric data source. While many proposed solutions boast high recognition accuracy, one major concern remains the effective protection of users’ iris data and prevention of privacy breaches. To address this issue, we propose an improved cancellable biometrics scheme based on the inversion fusion and local ranking strategy (IFCB), specifically targeting the vulnerability of the local ranking-based cancellable biometrics scheme (LRCB) to the ranking-inversion attack when recognition accuracy is high. The proposed method disrupts the original iris data by applying a random substitution string and rearranging blocks within each iris string. For every rearranged block, it is either inverted or kept unchanged. This combination of inversed and unchanged blocks, referred as inversion fusion, is then sorted to obtain rank values that are stored for subsequent matching. It is important to note that the inversion fusion step may lead to a loss of accuracy, which can be compensated by amplifying the iris data to improve accuracy. By utilizing a set of different random substitution strings, the rearranged iris strings are employed in both the inversion fusion and local ranking steps. A long iris template is generated and stored as the final protected iris template, forming the basis of the proposed IFCB method. Theoretical and experimental analyses demonstrate that the IFCB scheme effectively withstands rank-inversion attacks and achieves a favorable balance of accuracy, irreversibility, unlinkability, and revocability.