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Multimedia Tools and Applications

, Volume 78, Issue 22, pp 31557–31580 | Cite as

Cancelable fusion-based face recognition

  • Essam AbdellatefEmail author
  • Nabil A. Ismail
  • Salah Eldin S. E. Abd Elrahman
  • Khalid N. Ismail
  • Mohamed Rihan
  • Fathi E. Abd El-Samie
Article
  • 69 Downloads

Abstract

Biometric recognition refers to the automated process of recognizing individuals using their biometric patterns. Recent advancements in deep learning and computer vision indicate that generic descriptors which are extracted using convolutional neural networks (CNNs) could represent complex image characteristics. This paper presents a number of cancelable fusion-based face recognition (FR) methods; region-based, multi-biometric and hybrid-features. The former included methods incorporate the use of CNNs to extract deep features (DFs). A fusion network combines the DFs to obtain a discriminative facial descriptor. Cancelabilitiy is provided using bioconvolving as an encryption method. In the region-based method, the DFs are extracted from different face regions. The multi-biometric method uses different biometric traits to train multiple CNNs. The hybrid-features method merges the merits of deep-learned features and hand-crafted features to obtain a more representative output. Also, an efficient CNN model is proposed. Experimental results on various datasets prove that; (a) the proposed CNN model achieves remarkable results compared to other state-of-the-art CNNs, (b) region-based method is superior to multi-biometric and hybrid-features methods and (c) the utilization of bio-convolving method increases the system security with a slight degradation in the recognition accuracy.

Keywords

Deep features, Fusion network, and cancelable biometrics 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Essam Abdellatef
    • 1
    Email author
  • Nabil A. Ismail
    • 2
  • Salah Eldin S. E. Abd Elrahman
    • 2
  • Khalid N. Ismail
    • 3
    • 4
  • Mohamed Rihan
    • 5
  • Fathi E. Abd El-Samie
    • 5
  1. 1.Electronics and Communication DepartmentDelta Academy for EngineeringMansouraEgypt
  2. 2.Department of Computer Science and Engineering, Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt
  3. 3.Department of Computer ScienceDurham UniversityDurhamUK
  4. 4.Information Technology Department, Faculty of Computers and InformationMenoufia UniversityMenoufiaEgypt
  5. 5.Department of Electronics and Electrical Communications Engineering, Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

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