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Periocular Biometrics for Non-ideal Images Using Deep Convolutional Neural Networks

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Intelligent Computing and Communication (ICICC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1034))

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Abstract

The objective of this research is to study the effect of eyeglasses and the masking of the eye portion on the recognition accuracy of the periocular biometric authentication system. In this paper, six different off-the-shelf deep Convolutional Neural Networks (CNN) are implemented. Experimental results show that in both the cases VGG 19 CNN model outperforms others on the UBIPr database.

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Authors and Affiliations

  1. Computer Science Department, Indira Gandhi Delhi Technical University for Women, Delhi, India

    Punam Kumari & Seeja K. R.

Authors
  1. Punam Kumari
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  2. Seeja K. R.
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Corresponding author

Correspondence to Punam Kumari .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  3. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

  4. Department of MCA, J. S. S. Science and Technology University, Mysuru, India

    V. N. Manjunath Aradhya

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Kumari, P., R., S.K. (2020). Periocular Biometrics for Non-ideal Images Using Deep Convolutional Neural Networks. In: Bhateja, V., Satapathy, S., Zhang, YD., Aradhya, V. (eds) Intelligent Computing and Communication. ICICC 2019. Advances in Intelligent Systems and Computing, vol 1034. Springer, Singapore. https://doi.org/10.1007/978-981-15-1084-7_15

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