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Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques

Author

Listed:
  • Ahmed Sedik

    (Smart Systems Engineering Laboratory, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Department of the Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33511, Egypt)

  • Ahmed A. Abd El-Latif

    (EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt)

  • Mudasir Ahmad Wani

    (EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Fathi E. Abd El-Samie

    (Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
    Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia)

  • Nariman Abdel-Salam Bauomy

    (Electronics and Electrical Communications Department, Faculty of Engineering, Canadian International College (CIC), Giza 12511, Egypt)

  • Fatma G. Hashad

    (Department of Electrical Engineering, Higher Institute of Engineering and Technology, Kafr Elsheikh 33511, Egypt)

Abstract

Cybersecurity has been one of the interesting research fields that attract researchers to investigate new approaches. One of the recent research trends in this field is cancelable biometric template generation, which depends on the storage of a cipher (cancelable) template instead of the original biometric template. This trend ensures the confidential and secure storage of the biometrics of a certain individual. This paper presents a cancelable multi-biometric system based on deep fusion and wavelet transformations. The deep fusion part is based on convolution (Conv.), convolution transpose (Conv.Trans.), and additional layers. In addition, the deployed wavelet transformations are based on both integer wavelet transforms (IWT) and discrete wavelet transforms (DWT). Moreover, a random kernel generation subsystem is proposed in this work. The proposed kernel generation method is based on chaotic map modalities, including the Baker map and modified logistic map. The proposed system is implemented on four biometric images, namely fingerprint, iris, face, and palm images. Furthermore, it is validated by comparison with other works in the literature. The comparison reveals that the proposed system shows superior performance regarding the quality of encryption and confidentiality of generated cancelable templates from the original input biometrics.

Suggested Citation

  • Ahmed Sedik & Ahmed A. Abd El-Latif & Mudasir Ahmad Wani & Fathi E. Abd El-Samie & Nariman Abdel-Salam Bauomy & Fatma G. Hashad, 2023. "Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques," Mathematics, MDPI, vol. 11(3), pages 1-26, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:703-:d:1051345
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    Cited by:

    1. J. Alberto Conejero & Andrei Velichko & Òscar Garibo-i-Orts & Yuriy Izotov & Viet-Thanh Pham, 2024. "Exploring the Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps," Mathematics, MDPI, vol. 12(7), pages 1-23, March.

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