Negative Iris Recognition
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.
The iris is one of the most important biometrics, and it is more stable overthe entirelifetimeof a person compared withother widely used biometrics. According to, secure biometric recognition schemesthat arebased on the iris usually achieve betterresultsthan the schemes that are based on other biometrics. Therefore, in this paper, we pri-marilyfocus on constructing a secureiris recognition scheme.
The goal ofsecure biometric recognitionisto protect bi-ometric datawhile supportingeffectiverecognition. The main difficulty is to addressthe variance of the biometric data while transforming or encryptingbiometric data.Due to this difficulty,traditional techniques such as classical encryption algorithms (except forhomomorphicencryp-tion algorithms) cannotbe directly used for secure bio-metric recognition.
We proposenegative iris recognition, which isa novel and efficient secure iris recognition schemethat isbased on negative databases. We show that negative iris recognition supports im-portant strategies (i.e.,shifting and masking) in iris recognition, and we conductexperiments to verify the effectiveness of thesestrategies. We analyze the security and efficiency of negative iris recognition, andwedemonstrate its irreversibility, rev-ocability and renewability, and unlinkability. We conduct experiments to investigate the performance of negative iris recognition.