Flexible image segmentation and quality assessment for real-time iris recognition

The human iris has proved to be one of the most reliable biometric features for the identification of individuals. Real-time iris recognition requires high quality images that provide enough details about the iris texture and algorithms to analyze and process the images at the highest possible speed. In this work, an extension to the classical circular model for the pupil and iris using flexible contours is provided. Then, a method for assessing the quality of the iris images in real-time based on the segmentation results is introduced. Experimental results are presented, and we conclude that the new methods improve the recognition rate, achieving a 100% correct recognition rate on the CASIA iris database, while being suitable for a real-time iris recognition camera system.