Latent Spatial Features Based on Generative Adversarial Networks for Face Anti-spoofing

Jingtian Xia, Yan Tang, Xi Jia, Linlin Shen, Zhihui Lai

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


With the wide deployment of the face recognition system, many face attacks, such as print attack, video attack and 3D face mask, have emerged. Face anti-spoofing is very important to protect face recognition system from attack. This paper proposes a structure of generative adversarial networks with skip connection for face anti-spoofing. First, we obtain the latent spatial features of faces by training generative adversarial networks to reconstruct both real and spoof faces; second, we use the convolution neural networks to detect the spoofing faces. In this paper, the proposed method is evaluated by three public databases. The results suggest that our approach achieves as high as 98% accuracy on both CASIA-FASD and REPLAY-ATTACK databases.

Original languageEnglish
Title of host publicationBiometric Recognition - 14th Chinese Conference, CCBR 2019, Proceedings
EditorsZhenan Sun, Ran He, Shiguang Shan, Jianjiang Feng, Zhenhua Guo
Number of pages10
ISBN (Print)9783030314552
Publication statusPublished - 2019
Externally publishedYes
Event14th Chinese Conference on Biometric Recognition, CCBR 2019 - Zhuzhou, China
Duration: 12 Oct 201913 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11818 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Chinese Conference on Biometric Recognition, CCBR 2019


  • Face anti-spoofing
  • Generative adversarial networks
  • Latent spatial features

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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