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Efficient and Accurate Iris Detection and Segmentation Based on Multi-scale Optimized Mask R-CNN

  • Zhi LiEmail author
  • Di Miao
  • Huanwei Liang
  • Hui Zhang
  • Jing Liu
  • Zhaofeng He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11902)

Abstract

Iris segmentation plays an important role in iris recognition. However, traditional iris segmentation performance decreases dramatically on non-constrained conditions, which stops iris recognition system from being widely deployed. In this paper, an efficient and accurate iris detection and segmentation method based on multi-scale optimized Mask R-CNN method is proposed. The proposed method introduces the attention module and multi-scale fusion module to the iris segmentation task. The attention module accelerate the procedure by detecting a smaller iris region for segmentation, while the multi-scale fusion module faithfully preserves the explicit spatial position of iris region. Experimental results on UBIRIS.v2 and CASIA.v4-Distance demonstrate the superior performance of the proposed method.

Keywords

Iris segmentation Attention Multi-scale fusion 

Notes

Acknowledgement

This work was support by the National Key R & D Program of China [2018YFC0807303].

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhi Li
    • 1
    • 2
    Email author
  • Di Miao
    • 3
  • Huanwei Liang
    • 3
  • Hui Zhang
    • 3
  • Jing Liu
    • 3
  • Zhaofeng He
    • 3
  1. 1.Criminal Investigation Department of Public Security Bureau of Xinjiang Uygur Autonomous RegionXinjiang UygurChina
  2. 2.School of Economics and ManagementUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.Beijing IrisKing Tech Co., Ltd.BeijingChina

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