Iris Extraction Based on Intensity Gradient and Texture Difference
Biometrics has become more and more important in security applications. In comparison with many other bio- metric features, iris recognition has very high recognition accuracy. Successful iris recognition depends largely on correct iris localization, however, the performance of current techniques for iris localization still leaves room for improvement. To improve the iris localization performance, we propose a novel method that optimally utilizes both the intensity gradient and texture difference. Experimental results demonstrate that our new approach gives much better results than previous approaches. In order to make the iris boundary more accurate, we present a new issue called model selection and propose a method to choose between ellipse/circle and circle/circle models. Furthermore, we propose a dome model to compute mask images and remove eyelid occlusions in the unwrapped images rather than in the original eye images with a least commitment strategy.