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Presentation attack detection for iris recognition using deep learning

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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.

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Acknowledgements

We thank IIIT Delhi for providing us with the IIITD-WVU Mobile Iris Spoofing Dataset for the application of our approach.

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Authors and Affiliations

  1. Division of Computer Engineering, Netaji Subhas Institute of Technology, Delhi, India

    Shefali Arora & M. P. S. Bhatia

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  1. Shefali Arora
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  2. M. P. S. Bhatia
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Correspondence to Shefali Arora.

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Arora, S., Bhatia, M.P.S. Presentation attack detection for iris recognition using deep learning. Int J Syst Assur Eng Manag 11 (Suppl 2), 232–238 (2020). https://doi.org/10.1007/s13198-020-00948-1

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  • DOI: https://doi.org/10.1007/s13198-020-00948-1

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