Facial expression recognition using histogram variances faces

Ruo Du, Qiang Wu, Xiangjian He, Wenjing Jia, Daming Wei

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

6 Citations (Scopus)


In human's expression recognition, the representation of expression features is essential for the recognition accuracy. In this work we propose a novel approach for extracting expression dynamic features from facial expression videos. Rather than utilising statistical models e.g. Hidden Markov Model (HMM), our approach integrates expression dynamic features into a static image, the Histogram Variances Face (HVF), by fusing histogram variances among the frames in a video. The HVFs can be automatically obtained from videos with different frame rates and immune to illumination interference. In our experiments, for the videos picturing the same facial expression, e.g., surprise, happy and sadness etc., their corresponding HVFs are similar, even though the performers and frame rates are different. Therefore the static facial recognition approaches can be utilised for the dynamic expression recognition. We have applied this approach on the well-known Cohn-Kanade AU-Coded Facial Expression database then classified HVFs using PCA and Support Vector Machine (SVMs), and found the accuracy of HVFs classification is very encouraging.

Original languageEnglish
Title of host publication2009 Workshop on Applications of Computer Vision, WACV 2009
Publication statusPublished - 2009
Externally publishedYes
Event2009 Workshop on Applications of Computer Vision, WACV 2009 - Snowbird, UT, United States
Duration: 7 Dec 20098 Dec 2009

Publication series

Name2009 Workshop on Applications of Computer Vision, WACV 2009


Conference2009 Workshop on Applications of Computer Vision, WACV 2009
Country/TerritoryUnited States
CitySnowbird, UT

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Facial expression recognition using histogram variances faces'. Together they form a unique fingerprint.

Cite this