Automated Selection of Optimal Frames in NIR Iris Videos

A relatively new trend in the iris biometric area is the use of videos as a capturing device. Frame by frame approach is richer in information and gives more flexibility as opposed to the use of traditional still images. However, the quality, shape and size of the iris may vary from one frame to another. In this paper, we propose a new technique for selecting the best frames in an iris video. Taking advantage of the temporal correspondence in iris frames, we classify iris videos into 3 categories, namely Adequate, Motion Constrained and Time Constrained. Frames with blinks and off-angle gaze are eliminated using frame averaging and correlation. Quality factors, namely motion blur, out of focus, translational motion and lighting present in iris videos are detected and their effect on recognition performance is investigated. Experimental results are carried out on both the MBGC NIR Iris Video and the MBGC NIR Iris Still datasets from the National Institute for Standards and Technology (NIST). Firstly, this work demonstrates that the proposed optimal frame selection technique in NIR Iris Videos leads to significant improvement in recognition performance. Secondly, the performance of NIR Iris Still images vs. NIR Iris Videos is compared. Thirdly, we show that interoperability between iris frames and iris images in an iris recognition system affects performance. Finally, the computational time and the elimination of noisy frames at each stage using the proposed method are examined.