Abstract
In general, a typical iris recognition based Personal Identification System (PIS) includes iris imaging, iris image quality assessment, fake iris detection, and iris recognition. This paper presents a novel approach, which focusing on iris recognition. The novelty of this approach includes improving the speed and accuracy of the iris segmentation process, fetching the iris image so as to reduce the recognition error, producing a feature vector with discriminating texture features and a proper dimensionality so as to improve the recognition accuracy and computational efficiency. The Canny edge detection and circular Hough transforms are used for the segmentation process. The segmented iris is normalized using Daugman's rubber sheet model from –[32°,32°] and [148°,212°]. The phase data from 1D Log-Gabor filter is extracted and encoded efficiently to produce a proper feature vector. Experimental tests were performed using CASIAIrisV3 and UBIRIS iris databases. These tests prove that the proposed algorithm has an encouraging performance.
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