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Multi-instance cancellable biometrics schemes based on generative adversarial network

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Abstract

The main role of cancellable biometric schemes is to protect the privacy of the enrolled users. The protected biometric data are generated by applying a parametrized transformation function to the original biometric data. Although cancellable biometric schemes achieve high security levels, they may degrade the recognition accuracy. One of the mostwidely used approaches to enhance the recognition accuracy in biometric systems is to combine several instances of the same biometric modality. In this paper, two multi-instance cancellable biometric schemes based on iris traits are presented. The iris biometric trait is used in both schemes because of the reliability and stability of iris traits compared to the other biometric traits. A generative adversarial network (GAN) is used as a transformation function for the biometric features. The first scheme is based on a pre-transformation feature-level fusion, where the binary features of multiple instances are concatenated and inputted to the transformation phase. On the other hand, the second scheme is based on a post-transformation feature-level fusion, where each instance is separately inputted to the transformation phase. Experiments conducted on the CASIA Iris-V3-Internal database confirm the high recognition accuracy of the two proposed schemes. Moreover, the security of the proposed schemes is analyzed, and their robustness against two well-known types of attacks is proven.

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Authors and Affiliations

  1. Department of Computer Science, Mansoura University, Mansoura, 35516, Egypt

    Mayada Tarek, Eslam Hamouda & A. S. Abohamama

  2. Department of Computer Science, Jouf University, Jouf, 2014, Saudi Arabia

    Mayada Tarek & Eslam Hamouda

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  1. Mayada Tarek
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  2. Eslam Hamouda
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  3. A. S. Abohamama
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Correspondence to Mayada Tarek.

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Tarek, M., Hamouda, E. & Abohamama, A.S. Multi-instance cancellable biometrics schemes based on generative adversarial network. Appl Intell 52, 501–513 (2022). https://doi.org/10.1007/s10489-021-02401-7

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