In-the-wild facial expression recognition in extreme poses

Fei Yang, Qian Zhang, Chi Zheng, Guoping Qiu

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


In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.

Original languageEnglish
Title of host publicationNinth International Conference on Graphic and Image Processing, ICGIP 2017
EditorsHui Yu, Junyu Dong
ISBN (Electronic)9781510617414
Publication statusPublished - 2018
Event9th International Conference on Graphic and Image Processing, ICGIP 2017 - Qingdao, China
Duration: 14 Oct 201716 Oct 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference9th International Conference on Graphic and Image Processing, ICGIP 2017


  • Caffe
  • Facial expression
  • LBP
  • SIFT
  • SVM
  • deep learning
  • head pose
  • random forest

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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