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Real-Time Multiclass Face Spoofing Recognition Through Spatiotemporal Convolutional 3D Features

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Image Analysis and Processing - ICIAP 2023 Workshops (ICIAP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14365))

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Abstract

Face recognition is used in numerous authentication applications, unfortunately they are susceptible to spoofing attacks such as paper and screen attacks. In this paper, we propose a method that is able to recognise if a face detected in a video is not real and the type of attack performed on the fake video. We propose to learn the temporal features exploiting a 3D Convolution Network that is more suitable for temporal information. The 3D ConvNet, other than summarizing temporal information, allows us to build a real-time method since it is so much more efficient to analyse clips instead of analyzing single frames. The learned features are classified using a binary classifier to distinguish if the person in the clip video is real (i.e. live) or not, multi class classifier recognises if the person is real or the type of attack (screen, paper, ect.). We performed our test on 5 public datasets: Replay Attack, Replay Mobile, MSU-MSFD, Rose-Youtu, RECOD-MPAD.

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Acknowledgements

This work is partially funded by TIM S.p.A. through its UniversiTIM granting program.

Portions of the research in this paper used the Replay-Attack Dataset made available by the Idiap Research Institute, Martigny, Switzerland.

Portions of the research in this paper used the Replay-Mobile Dataset made available by the Idiap Research Institute, Martigny, Switzerland. Such Corpus was captured in collaboration with the Galician R and D Center on Advanced Telecommunications (GRADIANT), Vigo, Spain.

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Authors and Affiliations

  1. Image Processing Laboratory, Dipartimento di Matematica e Informatica, Universita’ degli studi di Catania, Viale A. Doria 6, 95125, Catania, Italy

    Salvatore Giurato, Alessandro Ortis & Sebastiano Battiato

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  1. Salvatore Giurato
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  2. Alessandro Ortis
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  3. Sebastiano Battiato
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Correspondence to Salvatore Giurato .

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Editors and Affiliations

  1. University of Udine, Udine, Italy

    Gian Luca Foresti

  2. University of Udine, Udine, Italy

    Andrea Fusiello

  3. University of York, York, UK

    Edwin Hancock

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Giurato, S., Ortis, A., Battiato, S. (2024). Real-Time Multiclass Face Spoofing Recognition Through Spatiotemporal Convolutional 3D Features. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14365. Springer, Cham. https://doi.org/10.1007/978-3-031-51023-6_30

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  • DOI: https://doi.org/10.1007/978-3-031-51023-6_30

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  • Online ISBN: 978-3-031-51023-6

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