Efficient iris segmentation method with support vector domain description - Tang, Rongnian; Han, Jiuqiang; Zhang, Xinman (Oficyna Wydawnicza Politechniki Wrocławskiej, 2009)
With the aim to improve the performance of iris segmentation method to process images with heterogeneous characteristics, the authors introduce a new method inspired by the support vector domain description (SVDD). A local geometric moment function is used to extract shape features of the iris borders. Then, these features are fed into the trained SVDD classifier for borders recognition followed by the application of Hough transform to solve circumference parameters of iris. The performance of the proposed method and the most cited methods, Daugman’s method and Wilders’ method, had been tested on the UBIRIS database. Compared with the two existing methods, our proposal is not only comparable to them when the iris image has good quality, but has better segmentation performance in the case of poor quality images. The experimental results show that the method proposed does have a higher robustness and is less dependent on the quality of raw iris image.
Optica Applicata, Vol. 39, 2009, nr 2, s. 365-374
Keywords: optyka; iris segmentation; local geometric moment; support vector domain description (SVDD); border recognition