An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning

Yung Hui Li, Po Jen Huang, Yun Juan

研究成果: 雜誌貢獻期刊論文同行評審

26 引文 斯高帕斯(Scopus)


Iris segmentation is a critical step in the entire iris recognition procedure. Most of the state-of-the-art iris segmentation algorithms are based on edge information. However, a large number of noisy edge points detected by a normal edge-based detector in an image with specular reflection or other obstacles will mislead the pupillary boundary and limbus boundary localization. In this paper, we present a combination method of learning-based and edge-based algorithms for iris segmentation. A well-designed Faster R-CNN with only six layers is built to locate and classify the eye. With the bounding box found by Faster R-CNN, the pupillary region is located using a Gaussian mixture model. Then, the circular boundary of the pupillary region is fit according to five key boundary points. A boundary point selection algorithm is used to find the boundary points of the limbus, and the circular boundary of the limbus is constructed using these boundary points. Experimental results showed that the proposed iris segmentation method achieved 95.49% accuracy on the challenging CASIA-Iris-Thousand database.

期刊Mobile Information Systems
出版狀態已出版 - 2019


深入研究「An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning」主題。共同形成了獨特的指紋。