Disentangled Feature Based Adversarial Learning for Facial Expression Recognition

Mengchao Bai, Weicheng Xie, Linlin Shen

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

11 Citations (Scopus)


A facial expression image can be considered as an addition of expressive component to a neutral expression face. With this in mind, in this paper, we propose a novel end-to-end adversarial disentangled feature learning (ADFL) framework for facial expression recognition. The ADFL framework is mainly composed of three branches: expression disentangling branch ADFL-d, neutral expression branch ADFL-n and residual expression branch ADFL-r. The ADFL-d and ADFL-n aim to extract the expressive component and neutral component, respectively. The ADFL-r extracts the residual expression by calculating the difference between feature maps of ADFL-d and ADFL-n, and uses the residual expression feature for expression classification. Experimental results on several benchmark databases (CK+, MMI and Oulu-CASIA) show that the proposed method has remarkable performance compared to state-of-the-art methods.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538662496
Publication statusPublished - Sept 2019
Externally publishedYes
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: 22 Sept 201925 Sept 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan, Province of China


  • adversarial learning
  • Disentangled feature
  • expression disentangling
  • residual expression

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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