Negative Iris Recognition

Elements of a person's biometrics are typically stable over the duration of a lifetime, and thus, it is highly important to protect biometric data while supporting recognition (it is also called secure biometric recognition). However, the biometric data that are derived from a person usually vary slightly due to a variety of reasons, such as distortion during picture capture, and it is difficult to use traditional techniques, such as classical encryption algorithms, in secure biometric recognition. The negative database (NDB) is a new technique for privacy preservation. Reversing the NDB has been demonstrated to be an NP-hard problem, and several algorithms for generating hard-to-reverse NDBs have been proposed. In this paper, first, we propose negative iris recognition, which is a novel secure iris recognition scheme that is based on the NDB. We show that negative iris recognition supports several important strategies in iris recognition, e.g., shifting and masking. Next, we analyze the security and efficiency of negative iris recognition. Experimental results show that negative iris recognition is an effective and secure iris recognition scheme. Specifically, negative iris recognition can achieve a highly promising recognition performance (i.e., GAR = 98.94% at FAR = 0.01%, EER = 0.60%) on the typical database CASIA-IrisV3-Interval.