With the development of the current networked society, personal identification based on biometrics has received more and more attention. Iris recognition has a satisfying performance due to its high reliability and non-invasion. In an iris recognition system, preprocessing, especially iris localization plays a very important role. The speed and performance of an iris recognition system is crucial and it is limited by the results of iris localization to a great extent. Iris localization includes finding the iris boundaries (inner and outer) and the eyelids (lower and upper). In this paper, we propose an iris localization algorithm based on texture segmentation. First, we use the information of low frequency of wavelet transform of the iris image for pupil segmentation and localize the iris with a differential integral operator. Then the upper eyelid edge is detected after eyelash is segmented. Finally, the lower eyelid is localized using parabolic curve fitting based on gray value segmentation. Extensive experimental results show that the algorithm has satisfying performance and good robustness.
The increasing demand on enhanced security has led to an unprecedented interest in automated personal identification based on biometrics. Among the various biometric identification methods, iris recognition is widely regarded as the most reliable and is one of the most active research topics in biometrics. Significant progress has been made since the concept of automated iris recognition was first proposed in 1987, not only in research and algorithm development but also in commercial exploitation and practical applications. This paper provides an overview on recent progress in iris recognition and discusses some of the remaining challenges and possible future work in this exciting field.