Abstract
Iris recognition is the most accurate biometric identification system on hand. Most iris recognition systems use algorithms developed by Daugman. The performance of iris recognition is highly depends on edge detection. Canny is the edge detectors which commonly used. The objectives of this research are to a) study the edge detection criteria and b) measure the PSNR values in estimating the noise between the original iris feature and new iris template. The eye image with [320×280] dimension is obtained from the CASIA database which has been pre-processed through the segmentation and normalization in obtaining the rubber sheet model with [20×240] in dimension. Once it has been produced, the important information is extracted from the iris. Results show that, the PSNR values of iris feature before and after the process of extraction, was 24.93 and 9.12. For sobel and prewitt, both give 18.5 after the process. Based on our findings, the impact of edge detection techniques produces higher accuracy in iris recognition system.
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