Iris Normalization Beyond Appr-Circular Parameter Estimation

Abstract

The requirement to recognize the iris image of low-quality is rapidly increasing with the practical application of iris recognition, especially the urgent need for high-throughput or applications in covert situations. The appr-circle fitting can not meet the needs due to the high time cost and non-accurate boundary estimation during the normalization process. Furthermore, the appr-circular hypothesis of iris and pupil is not entirely established due to the squint and occlusion in non-cooperative environments. To mitigate this problem, a multi-mask normalization without appr-circular parameter estimation is proposed to make full use of the segmented masks, which provide robust pixel-level iris boundaries. It bridges the segmentation and feature extraction to recognize the low-quality iris, which is thrown directly by the traditional methods. Thus, the complex samples with no appr-circular iris or massive occlusions can be recognized correctly. The extensive experiments are conducted on the representative and challenging databases to verify the generalization and the accuracy of the proposed iris normalization method. Besides, the throughput rate is significantly improved.