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)


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.


Iris segmentation Attention Multi-scale fusion 



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


  1. 1.
    Daugman, J.: Statistical richness of visual phase information: update on recognizing persons by iris patterns. Int. J. Comput. Vision 45(1), 25–38 (2001)CrossRefGoogle Scholar
  2. 2.
    Ma, L., Wang, Y., Tan, T.: Iris recognition using circular symmetric filters. In: Object Recognition Supported by User Interaction for Service Robots, vol. 2, pp. 414–417. IEEE (2002)Google Scholar
  3. 3.
    Tisse, C.-L., Martin, L., Torres, L., Robert, M., et al.: Person identification technique using human iris recognition. Proc. Vision Interface 294, 294–299 (2002)Google Scholar
  4. 4.
    Kong, W.K., Zhang, D.: Accurate iris segmentation based on novel reflection and eyelash detection model. In: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No. 01EX489), pp. 263–266. IEEE (2001)Google Scholar
  5. 5.
    Daugman, J.: How iris recognition works. In: The Essential Guide to Image Processing, pp. 715–739 (2009)Google Scholar
  6. 6.
    Daugman, J.: New methods in iris recognition. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 37(5), 1167–1175 (2007)CrossRefGoogle Scholar
  7. 7.
    He, Z., Tan, T., Sun, Z., Qiu, X.: Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(9), 1670–1684 (2009)CrossRefGoogle Scholar
  8. 8.
    Liu, N., Li, H., Zhang, M., Liu, J., Sun, Z., Tan, T.: Accurate iris segmentation in non-cooperative environments using fully convolutional networks. In: 2016 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2016)Google Scholar
  9. 9.
    Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar
  10. 10.
    Shah, S., Ross, A.: Iris segmentation using geodesic active contours. IEEE Trans. Inf. Forensics Secur. 4(4), 824–836 (2009)CrossRefGoogle Scholar
  11. 11.
    Li, Y.-H., Savvides, M.: An automatic iris occlusion estimation method based on high-dimensional density estimation. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 784–796 (2013)CrossRefGoogle Scholar
  12. 12.
    Tan, C.-W., Kumar, A.: Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans. Image Process. 21(9), 4068–4079 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Proenca, H.: Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1502–1516 (2010) CrossRefGoogle Scholar
  14. 14.
    Kawaguchi, T., Rizon, M.: Iris detection using intensity and edge information. Pattern Recogn. 36(2), 549–562 (2003)CrossRefGoogle Scholar
  15. 15.
    Mäenpää, T.: An iterative algorithm for fast iris detection. In: Li, S.Z., Sun, Z., Tan, T., Pankanti, S., Chollet, G., Zhang, D. (eds.) IWBRS 2005. LNCS, vol. 3781, pp. 127–134. Springer, Heidelberg (2005). Scholar
  16. 16.
    Bazrafkan, S., Thavalengal, S., Corcoran, P.: An end to end deep neural network for iris segmentation in unconstrained scenarios. Neural Networks 106, 79–95 (2018)CrossRefGoogle Scholar
  17. 17.
    Hu, J., Zhang, H., Xiao, L., Liu, J., He, Z., Li, L.: Seg-edge bilateral constraint network for iris segmentation. In: 2019 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2019)Google Scholar
  18. 18.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)Google Scholar
  19. 19.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  20. 20.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, June 2016Google Scholar
  21. 21.
    Lin, T.Y., Dollár, P., Girshick, R., He, K., Belongie, S.: Feature pyramid networks for object detection (2016)Google Scholar
  22. 22.
    Woo, S., Park, J., Lee, J.-Y., So Kweon, I.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)Google Scholar
  23. 23.
    Proenca, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.A.: The UBIRIS.v2: a database of visible wavelength images captured on-the-move and at-a-distance. IEEE Trans. PAMI 32(8), 1529–1535 (2010)CrossRefGoogle Scholar
  24. 24.
    Biometrics Ideal Test. CASIA.v4-database.
  25. 25.
    Zhao, Z., Ajay, K.: An accurate iris segmentation framework under relaxed imaging constraints using total variation model. In: The IEEE International Conference on Computer Vision (ICCV), December 2015Google Scholar
  26. 26.
    Tan, T., He, Z., Sun, Z.: Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vis. Comput. 28(2), 223–230 (2010). Segmentation of Visible Wavelength Iris Images Captured At-a-distance and On-the-moveCrossRefGoogle Scholar
  27. 27.
    Tan, C., Kumar, A.: Towards online iris and periocular recognition under relaxed imaging constraints. IEEE Trans. Image Process. 22(10), 3751–3765 (2013)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Tan, C., Kumar, A.: Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans. Image Process. 21(9), 4068–4079 (2012)MathSciNetCrossRefGoogle Scholar

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

Personalised recommendations