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Deep Biometrics pp 51–69Cite as

Anti-Spoofing in Face Recognition: Deep Learning and Image Quality Assessment-Based Approaches

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Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

Despite the fact that face recognition systems have improved significantly, the main concern of these systems remains its security against presentation attacks, so-called spoofing attacks. Therefore, it is important to develop techniques to automatically detect those attacks referred to as presentation attack detection (PAD) mechanisms. It is also important that these PAD mechanisms have to be seamlessly integrated into existing face recognition systems without harming the user experience. In this chapter, we address PAD in face recognition systems by proposing two fast and non-intrusive anti-spoofing methods. The first method is based on the combination of image quality measures (IQMs), while the second one is based on a multi-input architecture that combines a pre-trained CNN model and the local binary patterns (LBP) descriptor. Both approaches are extensively evaluated on different datasets. The obtained results outperformed state-of-the-art approaches. Moreover, our methods are well suited for real-time mobile applications and they are also privacy-compliant.

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Authors and Affiliations

  1. Worldline Company, Blois, France

    Wael Elloumi, Tarek Ben Charrada & Emna Fourati

  2. PRISME Laboratory, University of Orléans, Orléans, France

    Aladine Chetouani

Authors
  1. Wael Elloumi
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  2. Aladine Chetouani
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  3. Tarek Ben Charrada
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  4. Emna Fourati
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Corresponding author

Correspondence to Wael Elloumi .

Editor information

Editors and Affiliations

  1. School of Computing and Communication, InfoLab21, Lancaster University, Lancaster, UK

    Richard Jiang

  2. School of Information Technology, Deakin University, Waurn Ponds, VIC, Australia

    Chang-Tsun Li

  3. Electrical Engineering and Computer Science, School of Electronics, Queen’s University Belfast, Belfast, UK

    Danny Crookes

  4. Technical University of Denmark, Kgs. Lyngby, Denmark

    Weizhi Meng

  5. GREYC research Lab, ENSICAEN, Caen, France

    Christophe Rosenberger

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Elloumi, W., Chetouani, A., Charrada, T.B., Fourati, E. (2020). Anti-Spoofing in Face Recognition: Deep Learning and Image Quality Assessment-Based Approaches. In: Jiang, R., Li, CT., Crookes, D., Meng, W., Rosenberger, C. (eds) Deep Biometrics. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-32583-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-32583-1_4

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