Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (12): 148-155.doi: 10.11896/jsjkx.230500217

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Prior-guided Blind Iris Image Restoration Algorithm

WANG Jia1, XIANG Liuyu1, HUANG Yubo2, XIA Yufeng3, TIAN Qing4, HE Zhaofeng1   

  1. 1 School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876,China
    2 School of Integrated Circuits,Beijing University of Posts and Telecommunications,Beijing 100876,China
    3 School of Modern Post(School of Automation),Beijing University of Posts and Telecommunications,Beijing 100876,China
    4 School of Information,North China University of Technology,Beijing 100144,China
  • Received:2023-05-29 Revised:2023-09-13 Online:2023-12-15 Published:2023-12-07
  • About author:WANG Jia,born in 1996,Ph.D candidate,is a member of China Computer Federation.His main research in-terests include computer vision,image restoration,and iris recognition.
    HE Zhaofeng,born in 1982,Ph.D,professor,is a member of China Computer Federation.His main research interests include computer vision,biometrics,and reinforcement learning.
  • Supported by:
    National Natural Science Foundation of China(62176025,62106015,U21B2045).

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Abstract: As one of the most potential biometric technologies,iris recognition has been widely used in various industries.How-ever,the existing iris recognition system is easily disturbed by external factors during the image acquisition process,and the acquired iris images have inevitable problems of insufficient resolution and easy blurring.To address these challenges,a prior-guided blind iris image restoration method is proposed,which utilizes the generative adversarial network and iris priors to recover unknown degraded iris images mixed with degradation factors such as low resolution,motion blur,and out-of-focus blur.The network includes a degradation removal sub-network,a prior estimation sub-network,and a prior fusion sub-network.The prior estimation sub-network models the distribution of the style information of the input as prior knowledge to guide the generative network.Besides,the prior fusion sub-network uses an attentive fusion mechanism to integrate multi-level style features,which improves the utilization of information.Experimental results show that the proposed method outperforms other methods in both qualitative and quantitative indexes,achieves blind recovery of degraded irises,and improves the robustness of iris recognition.

Key words: Iris restoration, Iris recognition, Iris segmentation, Style information, Attentive fusion

CLC Number: 

  • TP391
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