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Analyzing Facial Temporal Patterns for Face Anti-Spoofing

Authors:

Linlin ShenAuthors Info & Claims

ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

Pages 200 - 207

https://doi.org/10.1145/3436369.3437404

Published: 11 January 2021 Publication History

Abstract

Face anti-spoofing is crucial as face recognition systems are widely challenged by the print attack and replay attack. Since facial temporal patterns of these attacks and real face are naturally different, this paper proposes two temporal modelling approaches to face anti-spoofing tasks. Firstly, we propose to analyze the temporal patterns of mid-level facial attributes in spectral domain, aiming to find the unique frequency patterns of real face and each attack, respectively. Then, we propose to directly model dynamics from the given data, by employing the dynamic image algorithm to generate low-level spatiotemporal representations of videos. In particular, we extract deep features from both global and local face parts, i.e. eyes, nose and mouth, and then fuse them for face spoofing detection. Then, a Convolutional Neural Networks (CNN) - Long Short-Term Memory (LSTM) units (CNN-LSTM) architecture is introduced to learn the high-level spatiotemporal features from dynamic facial images. The proposed approaches were evaluated on two benchmark databases. The results suggest the effectiveness of the second approaches, i.e. as low as 1.85% Equal Error Rate (EER) on CASIA-FASD and 0.00% Average Classification Error Rate (ACER) on REPLAY-ATTACK have been achieved.

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Cited By

Wang ZXu YWu LHan HMa YLi Z(2022)Improving Face Anti-spoofing via Advanced Multi-perspective Feature LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357566019:6(1-18)Online publication date: 8-Dec-2022
https://dl.acm.org/doi/10.1145/3575660

Index Terms

Analyzing Facial Temporal Patterns for Face Anti-Spoofing
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics

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ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

October 2020

552 pages

ISBN:9781450387835

DOI:10.1145/3436369

Copyright © 2020 ACM.

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Beijing University of Technology

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Published: 11 January 2021

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ICCPR 2020

ICCPR 2020: 2020 9th International Conference on Computing and Pattern Recognition

October 30 - November 1, 2020

Xiamen, China

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Cited By

Wang ZXu YWu LHan HMa YLi Z(2022)Improving Face Anti-spoofing via Advanced Multi-perspective Feature LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357566019:6(1-18)Online publication date: 8-Dec-2022
https://dl.acm.org/doi/10.1145/3575660

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