Toward more accurate iris recognition using dilated residual features

Kuo Wang, Ajay Kumar

Research output: Journal article publicationJournal articleAcademic researchpeer-review

68 Citations (Scopus)


Iris recognition has emerged as the more accurate, convenient, and low-cost biometric approach to authenticate human subjects. However, the accuracy offered by current popular iris recognition algorithms is below the expectations from the community, and therefore, researchers have recently focused their attention on deep learning-based methods. This paper investigates a new deep learning-based approach for iris recognition and attempts to improve the accuracy using a more simplified framework to more accurately recover the representative features. We consider residual network learning with dilated convolutional kernels to optimize the training process and aggregate contextual information from the iris images. Such an approach also alleviates the need for the down-sampling and up-sampling layers, which not only results in a simplified network but also results in outperforming matching accuracy over several classical and state-of-the-art algorithms for iris recognition, i.e., further improvement in equal error rates by 7.14%, 10.7%, and 27.4% on three test databases. In this paper, our reproducible experimental results are presented on three publicly available datasets that illustrate outperforming results and validate the usefulness of our approach.

Original languageEnglish
Article number8698817
Pages (from-to)3233-3245
Number of pages13
JournalIEEE Transactions on Information Forensics and Security
Issue number12
Publication statusPublished - Dec 2019


  • biometrics
  • deep learning
  • Iris recognition
  • personal identification

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


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