Watershed Based Iris SEgmentation

Recently, the research interest on biometric systems and applications has significantly grown up, aiming to bring the benefits of biometrics to the broader range of users. As signal processing and feature extraction play a very important role for biometric applications, they can be thought as a particular subset of pattern recognition techniques. Most of iris biometric systems have been designed for security applications and work on near-infrared (NIR) images. NIR images are not affected by illumination changes in visible light making systems working both in darker and lighter conditions. The reverse of the medal is a very short distance allowed between the acquisition camera and the user, further than a strictly controlled pose of the eye. For those reasons, the viability of NIR image based systems in commercial applications is quite limited. Several efforts have been devoted to designing new iris biometric approaches on color images acquired in visible wavelength light (VW). However, illumination changes significantly affect the iris pattern as well as the periocular region making both segmentation and feature extraction harder than in NIR. In the specific case of iris biometrics, segmentation represents a crucial aspect, as it must be fast as well as accurate. To this aim, a new watershed based approach for iris segmentation in color images is presented in this paper. The watershed transform is exploited to binarize an image of the eye, while circle fitting together with a ranking approach is applied to first approximate the iris boundary with a circle. The experimental results demonstrate this approach to be effective with respect to location accuracy.