ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (2): 395-412.doi: 10.7544/issn1000-1239.2020.20190092

• 人工智能 • 上一篇    下一篇

虹膜分割算法评价基准

王财勇, 孙哲南   

  1. (中国科学院大学人工智能学院 北京 100049) (模式识别国家重点实验室(中国科学院自动化研究所) 北京 100190) (wangcaiyong2017@ia.ac.cn)
  • 出版日期: 2020-02-01
  • 基金资助: 
    国家自然科学基金项目(U1836217,61427811);国家重点研发计划项目(2017YFC0821602)

A Benchmark for Iris Segmentation

Wang Caiyong and Sun Zhenan   

  1. (School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049) (National Laboratory of Pattern Recognition (Institute of Automation, Chinese Academy of Sciences), Beijing 100190)
  • Online: 2020-02-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (U1836217, 61427811) and the National Key Research and Development Program of China (2017YFC0821602).

摘要: 虹膜识别是生物特征识别中最稳定和最可靠的身份识别方法之一.在虹膜识别的整个流程中,虹膜分割处于预处理阶段,因此虹膜分割结果的好坏将直接影响虹膜识别的精度.自从1993年Daugman第1次提出高性能的虹膜识别系统以来,各种各样的虹膜分割算法陆续提出,尤其是近年来基于深度学习的虹膜分割算法极大地提升了虹膜分割的精度.然而,由于缺乏统一的数据库和评价指标,各种算法的性能比较杂乱而不公平,因此提出了一个公开的虹膜分割评价基准.首先,介绍了虹膜分割的定义和面临的挑战;其次全面梳理了3个有代表性的公开虹膜分割数据库,总结了其特点和挑战性;紧接着定义了虹膜分割的评价指标;然后对传统的和基于深度学习的虹膜分割算法进行了总结,并通过详细的实验对各类算法进行了比较和分析.实验结果表明:当前基于深度学习的虹膜分割算法在准确性上超越了传统的方法.最后,对基于深度学习的虹膜分割算法存在的问题进行了思考和讨论.

关键词: 生物特征识别, 虹膜识别, 虹膜分割, 深度学习, 语义分割

Abstract: Iris recognition has been considered as one of the most stable and reliable biometric identification technologies. In the whole process of iris recognition, iris segmentation is in the preprocessing stage, so the quality of iris segmentation can directly affect the accuracy of iris recognition. Since Daugman first proposed a high-performance iris recognition system in 1993, a variety of iris segmentation algorithms have been proposed. Especially in recent years, deep learning based iris segmentation algorithms have greatly improved the accuracy of iris segmentation. However, due to the lack of unified databases and evaluation protocols, the comparisons of different iris segmentation algorithms are messy and absence of fairness, hence an open iris segmentation evaluation benchmark is proposed. First, the definition and challenges of iris segmentation are briefly introduced. Second, a comprehensive summary of three representative, open iris segmentation databases including the characteristics and challenges is given. Next, evaluation protocols of iris segmentation are defined. Then the traditional iris segmentation algorithms and deep learning based iris segmentation algorithms are elaborated on and compared by extensive experiments. The experimental results show that deep learning based iris segmentation methods outperform the traditional approaches in terms of accuracy. Finally, we make in-depth discussions about open questions of deep learning based iris segmentation algorithms.

Key words: biometric identification, iris recognition, iris segmentation, deep learning, semantic segmentation

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