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Cancelable Template Generation Using Convolutional Autoencoder and RandNet

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1567))

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Abstract

The security of biometric systems has always been a challenging area of research to safeguard against the day-by-day introduction of new attacks with the advancement in technology. Cancelable biometric templates have proved to be an effective measure against these attacks while ensuring an individual’s privacy. The proposed scheme uses a convolutional autoencoder (CAE) for feature extraction, a rank-based partition network, and a random network to construct secured cancelable biometric templates. Evaluation of the proposed secured template generation scheme has been done on the face and palmprint modalities.

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Authors and Affiliations

  1. PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India

    Pankaj Bamoriya, Pritee Khanna & Aparajita Ojha

  2. Indian Institute of Technology, Roorkee, 247667, India

    Gourav Siddhad

Authors
  1. Pankaj Bamoriya
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  2. Gourav Siddhad
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  3. Pritee Khanna
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  4. Aparajita Ojha
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Corresponding author

Correspondence to Pritee Khanna .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Bamoriya, P., Siddhad, G., Khanna, P., Ojha, A. (2022). Cancelable Template Generation Using Convolutional Autoencoder and RandNet. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_32

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  • DOI: https://doi.org/10.1007/978-3-031-11346-8_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11345-1

  • Online ISBN: 978-3-031-11346-8

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