New recognition methods for human iris patterns

The use of user identification technology has become increasingly in demand in today´s society. Biometrics have been capitalized upon for this purpose. The human iris in particular is one of the most unique and intriguing biometrics available to use in the identification of an individual. The process of recognizing a human iris is split into four major steps. These steps are segmentation, normalization, feature extraction, and matching. In this paper, two new methods are introduced to implement feature extraction. Both methods use a sliding-window technique and different mathematical operations on the pixels to produce feature vectors. Experimental results of the methods produced relatively small feature vectors of size 5×120 and 5×130. A small feature vector is valuable as it contributes to the efficiency and speed of the overall recognition system. In addition, a step was included in both methods to minimize the effect of varying light intensity. This reduces the time needed to acquire an image with suitable lighting, which in turn contributes to the speed of the system as well. Analysis of our methods was done by considering various performance metrics such as the False Acceptance Rate (FAR), False Rejection Rate (FRR), and Recognition Rate (RR). Both proposed methods achieved a recognition rate of about 98.3264%.