A Face Spoofing Detection Method Based on Domain Adaptation and Lossless Size Adaptation

IEEE Access

Wenyun Sun¹  Yu Song¹  Zhong Jin²  Haitao Zhao³ 

[1] College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China;School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China;School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China;

关键词: Domain adaptation;    face anti-spoofing;    face liveness detection;    face presentation attack detection;    face spoofing detection;    forensics;   

DOI  :  10.1109/ACCESS.2020.2985453

来源: DOAJ

【 摘 要 】

In this paper, a face spoofing detection method called the Fully Convolutional Network with Domain Adaptation and Lossless Size Adaptation (FCN-DA-LSA) is proposed. As its name suggests, the FCN-DA-LSA includes a lossless size adaptation preprocessor followed by an FCN based pixel-level classifier embedded with a domain adaptation layer. The FCN local classifier makes full use of the basic properties of face spoof distortion namely ubiquitous and repetitive. The domain adaptation (DA) layer improves generalization across different domains. The lossless size adaptation (LSA) preserves the high-frequent spoof clues caused by the face recapture process. The ablation study shows that both DA and the LSA are necessary for high-accuracy face spoofing detection. The FCN-LSA obtains competitive performance among the state-of-the-art methods. With the help of small-sample external data in the target domain (2/50, 2/50, and 1/20 subjects for CASIA-FASD, Replay-Attack, and OULU-NPU respectively), the FCN-DA-LSA further improves the performance and outperforms the existing methods.

【 授权许可】

Unknown