Improving Iris Identification using User Quality and Cohort Information

Iris is one of the most distinguishable features of a human body, which remains fairly stable throughout the lifetime of an individual. This makes iris recognition one of the most reliable methods for biometric based identification. This paper investigates a new technique to improve the performance of the system by using cohort information and user-quality as the weight in the matching. The proposed approach uses the cohort information at the decision stage as cascaded classifiers. However, the second stage is only used if the first stage classifier is uncertain of its decision. The experimental results from the decision-level classifiers combination are presented, which show that the cascaded classification system significantly outperforms the single classifier, especially at lower value of FAR which is most likely to be the operating point for any system. This paper also proposes a new approach to ascertain the user-quality (iris) and illustrates its usage in the performance improvement.