Towards More Accurate Iris Recognition Using Deeply Learned Spatially Corresponding Features

Zijing Zhao, Ajay Kumar Pathak

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

68 Citations (Scopus)


This paper proposes an accurate and generalizable deep learning framework for iris recognition. The proposed framework is based on a fully convolutional network (FCN), which generates spatially corresponding iris feature descriptors. A specially designed Extended Triplet Loss (ETL) function is introduced to incorporate the bit-shifting and non-iris masking, which are found necessary for learning discriminative spatial iris features. We also developed a sub-network to provide appropriate information for identifying meaningful iris regions, which serves as essential input for the newly developed ETL. Thorough experiments on four publicly available databases suggest that the proposed framework consistently outperforms several classic and state-of-the-art iris recognition approaches. More importantly, our model exhibits superior generalization capability as, unlike popular methods in the literature, it does not essentially require database-specific parameter tuning, which is another key advantage over other approaches.
Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
Number of pages10
ISBN (Electronic)9781538610329
Publication statusPublished - 22 Dec 2017
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice Convention Center, Venice, Italy
Duration: 22 Oct 201729 Oct 2017


Conference16th IEEE International Conference on Computer Vision, ICCV 2017

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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