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Efficient chaotic-Baker-map-based cancelable face recognition

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Abstract

Biometric recognition schemes are commonly utilized for security purposes. These schemes face two major challenges: security challenge and reliance on a particular biometric for authentication. The protection challenge comes from the use of basic biometrics in records. Therefore, if these records are compromised, the biometrics will no longer be valid. Consequently, it is necessary to preserve basic biometrics by protecting them from being used in biometric records. Cancelable biometric recognition systems rely on changing the information or biometric features to different formats, so that persons can use their specific biometric models in single or multiple systems. The mixture of chaos theory and cryptography shapes a crucial area for information security. The newest development in encryption systems is chaos-based for various distinctive attributes such as sensitivity to preliminary conditions, non-convergence, non-periodicity, and control parameters. This paper introduces a method to produce numerous encrypted biometric templates that are re-created by various convolution kernels generated from employing chaotic Baker map in different domains. Our proposed method has superior performance in treating variations in illumination, occlusion and facial expressions. It also has the added novelty of being able to perform authentication in the encrypted domain. In addition, the same approach can be applied on different databases. The chaotic map effect in different domains is evaluated on the widely-used AT&T, YALE, UFI, LFW, and FERET databases. The cancelable biometric system using the proposed Discrete Wavelet Transform (DWT) domain encryption with various keys has the best performance among all other implementations. In average, we achieved a 2% error probability, a 0.3 s authentication time, a 0.02% False Rejection Rate (FRR), a 0% False Acceptance Rate (FAR), a 0.0% Equal Error Rate (EER) and a 98.43% accuracy. Our proposed method also provides template diversity.

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Acknowledgements

The authors would like to thank the Deanship of Scientific Research, Taif University Researchers Supporting Project number (TURSP-2020/08), Taif University, Taif, Saudi Arabia for supporting this research work.

Funding

This study was funded by the Deanship of Scientific Research, Taif University Researchers Supporting Project number (TURSP-2020/08), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Osama S. Faragallah

  2. Electrical Department, Faculty of Technology and Education, Suez University, Suez, 43527, Egypt

    Ensherah A. Naeem

  3. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Walid El-Shafai, Noha Ramadan, Hossam El-din H. Ahmed & Fathi E. Abd El-Samie

  4. Department of Electronics and Electrical Communications, Faculty of Engineering, Tanta University, Tanta, Egypt

    Mustafa M. Abd Elnaby

  5. Department of Electronics and Electrical Communications, Kafrelsheikh University, Kafrelsheikh, 61519, Egypt

    Ibrahim Elashry

  6. Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt

    Said E. El-khamy

  7. Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Walid El-Shafai

  8. Department of Electrical Engineering, Ahram Canadian University, 6th of October, Egypt

    Noha Ramadan

  9. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 21974, Saudi Arabia

    Fathi E. Abd El-Samie

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  1. Osama S. Faragallah
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  2. Ensherah A. Naeem
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  8. Said E. El-khamy
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  9. Fathi E. Abd El-Samie
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Correspondence to Ensherah A. Naeem.

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Faragallah, O.S., Naeem, E.A., El-Shafai, W. et al. Efficient chaotic-Baker-map-based cancelable face recognition. J Ambient Intell Human Comput 14, 1837–1875 (2023). https://doi.org/10.1007/s12652-021-03398-0

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