Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (2): 330-336.doi: 10.11896/jsjkx.200100020

• Information Security • Previous Articles    

Cross-domain Few-shot Face Spoofing Detection Method Based on Deep Feature Augmentation

SUN Wen-yun1, JIN Zhong2, ZHAO Hai-tao3, CHEN Chang-sheng1   

  1. 1 Shenzhen Key Laboratory of Media Security, College of Electronics, Information Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
    2 School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
    3 School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China
  • Received:2020-01-03 Revised:2020-04-23 Online:2021-02-15 Published:2021-02-04
  • About author:SUN Wen-yun,born in 1987,Ph.D.His main research interests include deep learning and facial image analysis.
    CEHN Chang-sheng,born in 1986,Ph.D,lecturer,postgraduate supervisor,is a member of China Compu-ter Federation.His main research interests include 2D barcode,pattern recognition,machine learning and information security.
  • Supported by:
    The National Natural Science Foundation of China(61902250,61702340,61872188),National Basic Research Program of China(2014CB349303),China Postdoctoral Science Foundation(2018M643183),Natural Science Foundation of Guangdong Province(2017A030310382) and ShenzhenBasic Research and Free Exploration Project(JCYJ20180305124550725,827/000213).

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Abstract: The face recognition technology is improving rapidly these days.One the other side,the face presentation attack has become a practical security problem.To protect the system,face presentation attack detection methods are employed for detecting such attacks in advance.This paper extends a classic domain adaptation method to the deep neural network scenario,defines a feature augmentation-based domain adaptation layer,proposes a cross-domain few-shot face presentation attack detection method based on deep feature augmentation.This method is based on the existing method based on Fully Convolutional Network and improves the existing method by embedding a domain adaptation layer in the middle of the network.The new layer augments the feature maps,adapts the difference between the source and target domains.Then,a pixel-level probability map is predicted based on the augmented the feature maps.Finally,the prediction map is fused to a frame-level decision.Experiments are conducted on the CASIA-FASD,Replay-Attack and OULU-NPU datasets.Six commonly used protocols including the cross-dataset protocols between CASIA-FASD and Replay-Attack,the standard protocols of the OULU-NPU dataset are followed.The training and test data are cross different backgrounds,presentation attack instruments and cameras.The experiment results show that the baseline method,the Fully Convolutional Networkbased face presentation attack detection method has already achieved state-of-the-art performance.The performance can be further improved by learning the domain adaptation model on small-sample data in the target domain.The proposed method can halve the error rate by introducing domain adaptation (train on CASIA-FASD and test on Replay-Attack:decreased from 27.31% to 11.23%,train on Replay-Attack and test on CASIA-FASD:decreased from 37.33% to 21.83%,OULU-NPU's standard protocol IV:decreased from 9.45% to 5.56%).This confirms the effectiveness of the proposed method.

Key words: Deep learning, Face spoofing detection, Facial image analysis, Pattern recognition, System security

CLC Number: 

  • TP311
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