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S-DCTNet: Security-oriented biometric feature extraction technique

An effective pathway to secure and reliable biometric systems

  • 1171: Real-time 2D/ 3D Image Processing with Deep Learning
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Abstract

The proliferation of information technology has prompted researchers to create a multitude of new security solutions for secure electronic applications, especially on the Internet. Among them, security officials prefer authentication systems for user’s identity identification. Indeed, biometric authentication has proved to be superior in many respects compared to the traditional authentication means. Unfortunately, these systems are vulnerable to a variety of attacks, the most serious of which is perhaps the attack on the stored or transmitted template, which makes the safety of this template more important in the design of the biometric systems. This research, therefore, suggests an effective feature extraction method that can provide a deep and cancelable biometric feature. In this study, DCTNet deep learning is combined with chaotic systems to extract revocable palmprint/palm-vein features.

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Acknowledgements

This work would not have been possible without the financial support of the Directorate General for Scientific Research and Technological Development (DGRSDT). The authors also appreciate the unknown referee’s valuable and profound comments.

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Authors and Affiliations

  1. Laboratory of Mathematics, Informatics and Systems (LAMIS), University of Larbi Tebessi, Tebessa, Algeria

    Mohamed Yassine Haouam, Abdallah Meraoumia, Lakhdar Laimeche & Issam Bendib

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  1. Mohamed Yassine Haouam
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  2. Abdallah Meraoumia
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  3. Lakhdar Laimeche
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  4. Issam Bendib
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Correspondence to Mohamed Yassine Haouam.

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Haouam, M.Y., Meraoumia, A., Laimeche, L. et al. S-DCTNet: Security-oriented biometric feature extraction technique. Multimed Tools Appl 80, 36059–36091 (2021). https://doi.org/10.1007/s11042-021-10936-7

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  • DOI: https://doi.org/10.1007/s11042-021-10936-7

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