@inproceedings{843435beaf964dccabeede5451d1ee8c,
title = "Visible Spectral Iris Segmentation via Deep Convolutional Network",
abstract = "Iris segmentation is the prerequisite for the precise iris recognition. Visible spectral iris images may result in lower segmentation accuracy due to noise interference. We use deep learning method to segment the iris region in visible spectral iris images. A deep convolution neural network is designed to extract the eye features and segment the iris, pupil, sclera and background. It{\textquoteright}s an end-to-end model which requires no further processing. We collect the eye images and manually mask different part of the eye to establish the visible spectral iris dataset for training and testing. The proposed method was trained based on DeepLab framework. Experimental results show that the proposed method has efficiency on iris segmentation.",
keywords = "Convolution neural network, Deep learning, Iris segmentation",
author = "Yuqing He and Saijie Wang and Kuo Pei and Mingqi Liu and Jiawei Lai",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 12th Chinese Conference on Biometric Recognition, CCBR 2017 ; Conference date: 28-10-2017 Through 29-10-2017",
year = "2017",
doi = "10.1007/978-3-319-69923-3_46",
language = "English",
isbn = "9783319699226",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "428--435",
editor = "Yunhong Wang and Yu Qiao and Jie Zhou and Jianjiang Feng and Zhenan Sun and Zhenhua Guo and Shiguang Shan and Linlin Shen and Shiqi Yu and Yong Xu",
booktitle = "Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings",
address = "Germany",
}