Abstract
Iris recognition is one of the most powerful techniques for biometric identification. The requirement for smart environments is to acquire multiple iris codes from the same eye and evaluate which bits are the most consistent bits in the iris code. When the acquired images are noisy, the inconsistent bits in the iris code should be masked to improve performance. This paper thoroughly investigates the use of multiple training samples for enrollment. Based on this, an enhanced iris recognition approach is proposed for the smart environments employing the fusion of a set of iris images of a given eye using the most consistent feature data. The algorithm reduces the database size and accelerates the matching process. The Chinese Academy of Sciences - Institute of Automation (CASIA) database is used to simulate the studies. The comparison of probe to multiple gallery samples in the proposed approach has been shown to improve the performance of the system compared to the existing Daugman algorithm.
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