Abstract
Today, biometrics are the preferred technologies for person identification, authentication, and verification cutting across different applications and industries. Sadly, this ubiquity has invigorated criminal efforts aimed at violating the integrity of these modalities. Our study presents a multi-biometric cancellable scheme (MBCS) that exploits the proven utility of deep learning models to fuse multi-exposure fingerprint, finger vein, and iris biometrics by using an Inspection V3 pre-trained model to generate an aggregate tamper-proof cancellable template. To validate our MBCS, we employed an extensive evaluation including visual, quantitative, and qualitative assessments as well as complexity analysis where average outcomes of 99.158%, 24.523 dB, 0.079, 0.909, 59.582 and 23.627 were recorded for NPCR, PSNR, SSIM, UIQ, SD and UACI respectively. These quantitative outcomes indicate that the proposed scheme compares favourably against state-of-the-art methods reported in the literature. To further improve the utility of the proposed MBCS, we are exploring its refinement to facilitate generation of cancellable templates for real-time biometric applications in person authentication at airports, banks, etc.
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Acknowledgements
This study is sponsored in full by the Prince Sattam Bin Abdulaziz University, Saudi Arabia via the Deanship for Scientific Research funding for the Advanced Computational Intelligence and Intelligent Systems Engineering (ACIISE) Research Group Project number 2020/01/12173. Support from the Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-Koom, Egypt is appreciated.
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El-Rahiem, B.A., Amin, M., Sedik, A. et al. An efficient multi-biometric cancellable biometric scheme based on deep fusion and deep dream. J Ambient Intell Human Comput 13, 2177–2189 (2022). https://doi.org/10.1007/s12652-021-03513-1
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DOI: https://doi.org/10.1007/s12652-021-03513-1