计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (6): 186-197.doi: 10.11896/jsjkx.231200175

• 计算机图形学&多媒体 • 上一篇    下一篇

异质虹膜识别研究综述

孔佳琳1, 张琪1, 王财勇2   

  1. 1 中国人民公安大学信息网络安全学院 北京 100038
    2 北京建筑大学电气与信息工程学院 北京 100044
  • 收稿日期:2023-12-25 修回日期:2024-02-16 出版日期:2024-06-15 发布日期:2024-06-05
  • 通讯作者: 张琪(qi.zhang@ppsuc.edu.cn)
  • 作者简介:(lynn00660@163.com)
  • 基金资助:
    国家自然科学基金(61906199,62106015);中国人民公安大学课程建设项目(2022KCJS026)

Review of Heterogeneous Iris Recognition

KONG Jialin1, ZHANG Qi1, WANG Caiyong2   

  1. 1 School of Information and Cyber Security,People’s Public Security University of China,Beijing 100038,China
    2 School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China
  • Received:2023-12-25 Revised:2024-02-16 Online:2024-06-15 Published:2024-06-05
  • About author:KONG Jialin,born in 2000,M.S.candidate,is a member of CCF(R3399G).Her main research interests include artificial intelligence and biometrics,etc.
    ZHANG Qi,born in 1988,Ph.D,lectu-rer.Her main research interests include biometrics,machine learning,etc.
  • Supported by:
    National Natural Science Foundation of China(61906199,62106015) and Curriculum Construction Project of People’s Public Security University of China(2022KCJS026) .

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摘要： 虹膜图像采集环境和设备的不同导致虹膜注册和识别样本差异较大,给传统的虹膜识别技术带来了挑战。异质虹膜识别问题已成为学术界和工业界关注的焦点。文中从不同层级、样本差异性以及单源和多源3个角度对现有的异质虹膜识别方法进行了分类和综述,总结了目前异质虹膜识别的最新进展。按照跨质量、跨设备和跨光谱的分类对现有的异质虹膜数据集进行综述,并总结概述虹膜识别评价指标,以便研究人员更好地评估和验证算法的性能。最后,从环境鲁棒性、数据异质性建模和多模态融合3个方向,对未来异质虹膜识别研究的发展方向进行了展望。

关键词: 虹膜识别, 异质图像, 生物特征, 深度学习

Abstract: The variations in iris image acquisition environment and devices result in significant disparities in iris registration and recognition samples,which brings challenges to the traditional iris recognition technology.Heterogeneous iris recognition has emerged as a focal point of interest in both academic and industrial domains.This paper classifies and summarizes the existing he-terogeneous iris recognition methods from three perspectives:different levels,sample distinctiveness,and single-source versus multi-source scenarios,and summarizes the latest advancements in heterogeneous iris recognition.Existing heterogeneous iris datasets are reviewed according to the classification of cross-quality,cross-device and cross-spectrum,and the iris recognition evaluation metrics are summarized so that researchers can better evaluate and validate the algorithm performance.Finally,the future development direction of heterogeneous iris recognition is prospected,focusing on three aspects:environmental robustness,modeling of data heterogeneity and multimodal fusion.

Key words: Iris recognition, Heterogeneous images, Biometrics, Deep learning

中图分类号: 

  • TP391.41
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