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Multi-channel Face Presentation Attack Detection Using Deep Learning

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Deep Learning-Based Face Analytics

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Face recognition has emerged as a widely used biometric modality. However, its vulnerability to presentation attacks remains a significant security threat. Although Presentation Attack Detection (PAD) methods attempt to remedy this problem, often they fail in generalizing to unseen attacks and environments. As the quality of presentation attack instruments improves over time, achieving reliable PA detection using only visual spectra remains a major challenge. We argue that multi-channel systems could help solve this problem. In this chapter, we first present an approach based on a multi-channel convolutional neural network for the detection of presentation attacks. We further extend this approach to a one-class classifier framework by introducing a novel loss function that forces the network to learn a compact embedding for the bonafide class while being far from the representation of attacks. The proposed framework introduces a novel way to learn a robust PAD system from bonafide and available (known) attack classes. The superior performance in unseen attack samples in publicly available multi-channel PAD database WMCA shows the effectiveness of the proposed approach. Software, data, and protocols for reproducing the results are made publicly available.

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Notes

  1. 1.

    The term spoofing should be deprecated in favor of presentation attacks to comply with ISO standards.

  2. 2.

    Source code: https://gitlab.idiap.ch/bob/bob.paper.oneclass_mccnn_2019.

  3. 3.

    Database available at: https://www.idiap.ch/dataset/wmca.

  4. 4.

    Available from: https://github.com/AlexanderParkin/ChaLearn_liveness_challenge.

  5. 5.

    Source code: https://gitlab.idiap.ch/bob/bob.paper.oneclass_mccnn_2019.

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Acknowledgements

Part of this research is based on work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2017-17020200005. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

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  1. Idiap Research Institute, Centre du Parc, Rue Marconi 19, 1920, Martigny, CH, Switzerland

    Anjith George & Sébastien Marcel

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  1. Anjith George
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  2. Sébastien Marcel
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Correspondence to Anjith George .

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  1. Department of Computer Science and Engineering, University at Buffalo-SUNY, Buffalo, NY, USA

    Nalini K Ratha

  2. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA

    Vishal M. Patel

  3. Departments of Electrical and Computer Engineering (Whiting School of Engineering) and Biomedical Engineering (School of Medicine), Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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George, A., Marcel, S. (2021). Multi-channel Face Presentation Attack Detection Using Deep Learning. In: Ratha, N.K., Patel, V.M., Chellappa, R. (eds) Deep Learning-Based Face Analytics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-74697-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-74697-1_13

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