Robust iris segmentation algorithm in non‐cooperative environments using interleaved residual u‐net

33Citations
Citations of this article
20Readers
Mendeley users who have this article in their library.

Abstract

Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolu-tion, off‐axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called “Inter-leaved Residual U‐Net” (IRUNet) for semantic segmentation and iris mask synthesis. The K‐means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respec-tively, which outperforms the existing approaches on the challenging CASIA‐Iris‐Thousand data-base.

Cite

CITATION STYLE

APA

Li, Y. H., Putri, W. R., Aslam, M. S., & Chang, C. C. (2021). Robust iris segmentation algorithm in non‐cooperative environments using interleaved residual u‐net. Sensors, 21(4), 1–21. https://doi.org/10.3390/s21041434

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free