| Summary: | 碩士 === 國立中央大學 === 軟體工程研究所 === 104 === Iris segmentation is one of the most important pre-processing stage for an iris recognition system. The quality of iris segmentation results dictates the iris recognition performance. In the past, methods of either learning-based (for example, neural network) or non-learning-based (for example, Hough Transform) have been proposed to deal with this topic. However, there does not exist an objective and quantitative figure of merit in terms of quality assessment for iris segmentation (to judge whether a segmentation hypothesis is accurate or not). Most existing works evaluated their iris segmentation quality by human. In this work, we propose KIRD, a mechanism to fairly judge the correctness of iris segmentation hypotheses. On the foundation of KIRD, we propose AILIS, which is an adaptive and iterative learning method for iris segmentation. AILIS is able to learn from past experience and automatically build machine-learning models for iris segmentation for both gray-scale and colored iris images. Experimental results show that, without any prior training, AILIS can successfully perform iris segmentation on ICE (gray-scale images) and UBIRIS (colored) to the accuracy rate of 99.39% and 94.60%, respectively. Large-scale iris recognition experiments based on AILIS segmentation hypotheses also validated its effectiveness, compared to the state-of-the-art algorithm.
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