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Face Templates Encryption Technique Based on Random Projection and Deep Learning

Mayada Tarek1,2,*

1 Department of Computer Science, Mansoura University, Mansoura, 35516, Egypt
2 Department of Computer Science, Jouf University, Jouf, 2014, KSA

* Corresponding Author: Mayada Tarek. Email:

Computer Systems Science and Engineering 2023, 44(3), 2049-2063.


Cancellable biometrics is the solution for the trade-off between two concepts: Biometrics for Security and Security for Biometrics. The cancelable template is stored in the authentication system’s database rather than the original biometric data. In case of the database is compromised, it is easy for the template to be canceled and regenerated from the same biometric data. Recoverability of the cancelable template comes from the diversity of the cancelable transformation parameters (cancelable key). Therefore, the cancelable key must be secret to be used in the system authentication process as a second authentication factor in conjunction with the biometric data. The main contribution of this paper is to tackle the risks of stolen/lost/shared cancelable keys by using biometric trait (in different feature domains) as the only authentication factor, in addition to achieving good performance with high security. The standard Generative Adversarial Network (GAN) is proposed as an encryption tool that needs the cancelable key during the training phase, and the testing phase depends only on the biometric trait. Additionally, random projection transformation is employed to increase the proposed system’s security and performance. The proposed transformation system is tested using the standard ORL face database, and the experiments are done by applying different features domains. Moreover, a security analysis for the proposed transformation system is presented.


Cite This Article

M. Tarek, "Face templates encryption technique based on random projection and deep learning," Computer Systems Science and Engineering, vol. 44, no.3, pp. 2049–2063, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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