One-Class Face Anti-spoofing Based on Attention Auto-encoder

Xiaobin Huang, Jingtian Xia, Linlin Shen

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

1 Citation (Scopus)

Abstract

Face anti-spoofing (FAS) is crucial to defense spoofing attack against face recognition system. Most of existing methods use a large number of attack samples to train the classification model, which requires high computational and labelling costs. It’s also not flexible to collect large number of attack sample each time a new attack model is invented. To address the issue, we propose an Attention Auto-Encoder (AAE) based one-class FAS model in this paper. As only real face samples are required for training, the generalization capability of our method can be significantly improved. In addition, for FAS tasks, attention-based model can filter out irrelevant information and pay attention to consistent feature of genuine face. We use reconstruction error and the latent layer of AAE network to calculate the spoofness score to evaluate the proposed approach. Comprehensive experiments on CASIA-FASD and REPLAY-ATTACK databases show that our method achieves superior performance on cross-dataset testing, i.e., 20.0% and 26.9% HTER is achieved. The results suggest that our method is much more robust against attack patterns not available in the training set.

Original languageEnglish
Title of host publicationBiometric Recognition - 15th Chinese Conference, CCBR 2021, Proceedings
EditorsJianjiang Feng, Junping Zhang, Manhua Liu, Yuchun Fang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages365-373
Number of pages9
ISBN (Print)9783030866075
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event15th Chinese Conference on Biometric Recognition, CCBR 2021 - Shanghai, China
Duration: 10 Sep 202112 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12878 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th Chinese Conference on Biometric Recognition, CCBR 2021
Country/TerritoryChina
CityShanghai
Period10/09/2112/09/21

Keywords

  • Attention auto-encoder
  • Face anti-spoofing
  • Loss function
  • One-class
  • Spoofness score

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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