Hybrid Fusion Framework for Iris Recognition Systems

  • He Zhang
  • Jing LiuEmail author
  • Zhiguo Zeng
  • Qianli Zhou
  • Shengguang Li
  • Xingguang Li
  • Hui Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)


Due to the advantages in uniqueness, convenience and non-contact, iris recognition is widely deployed for automatic identity authentication. Instead of a single signature, multiple templates are registered in real-world applications for the diversity of gallery samples, resulting in great enhanced user experience. In this paper, we exploit the connection among the multiple registration data and then make efforts to give a more comprehensive decision based on them. A novel hybrid fusion framework is proposed to fuse information at groups in feature and score levels. Specifically, the gallery samples are firstly divided into groups to balance the abundance and the robustness of information. Afterwards, hierarchical fusion is performed at the groups, which is actually the procedure of information mapping and reducing. The experimental results demonstrate the effectiveness and generalization ability of the proposed hybrid fusion framework.


Iris recognition Hybrid fusion Feature level fusion Score level fusion 



This work is supported by the Natural Science Foundation of China (61503365).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • He Zhang
    • 1
    • 2
  • Jing Liu
    • 1
    Email author
  • Zhiguo Zeng
    • 3
  • Qianli Zhou
    • 3
  • Shengguang Li
    • 4
  • Xingguang Li
    • 1
  • Hui Zhang
    • 1
  1. 1.Beijing IrisKing Co., Ltd.BeijingChina
  2. 2.Beihang University of ChinaBeijingChina
  3. 3.Beijing Municipal Public Security BureauBeijingChina
  4. 4.First Research Institute of The Ministry of Public Security of PRCBeijingChina

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