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Presentation attack detection for iris recognition using deep learning

Author

Listed:
  • Shefali Arora

    (Netaji Subhas Institute of Technology)

  • M. P. S. Bhatia

    (Netaji Subhas Institute of Technology)

Abstract

Iris recognition is used in various applications to identify a person. However, presentation attacks are making such systems vulnerable. Intruders can impersonate an individual to get entry into a system. In this paper, we have focused on print attacks, in which an intruder can use various techniques like printing of iris photographs to present to the sensor. Experiments conducted on the IIIT-WVU iris dataset show that print attack images of live iris images, use of contact lenses and conjunction of both can play a significant role in deceiving the iris recognition systems. The paper makes use of deep Convolutional Neural Networks to detect such spoofing techniques with superior results as compared to the existing state-of-the-art techniques.

Suggested Citation

  • Shefali Arora & M. P. S. Bhatia, 2020. "Presentation attack detection for iris recognition using deep learning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 232-238, July.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:2:d:10.1007_s13198-020-00948-1
    DOI: 10.1007/s13198-020-00948-1
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    Cited by:

    1. N. S. Bhadauria & Indrajeet Kumar & H. S. Bhadauria & R. B. Patel, 2021. "Hemorrhage detection using edge-based contour with fuzzy clustering from brain computed tomography images," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1296-1307, December.

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