Facial expression analysis by machine learning

Siu Yeung Cho, Teik Toe Teoh, Yok Yen Nguwi

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review

1 Citation (Scopus)


Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face that results in temporally deformed facial features like wide-open mouth, raising eyebrows or etc. The challenges of such system have to address with some issues. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometimes, conventional classifiers are not even effective in handling those features and produce good classification performance. This chapter discusses the issues on how the advanced feature selection techniques together with good classifiers can play a vital important role of real-time facial expression recognition. Several feature selection methods and classifiers are discussed and their evaluations for real-time facial expression recognition are presented in this chapter. The content of this chapter is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.

Original languageEnglish
Title of host publicationAdvances in Face Image Analysis
Subtitle of host publicationTechniques and Technologies
PublisherIGI Global
Number of pages20
ISBN (Print)9781615209910
Publication statusPublished - 2010
Externally publishedYes

ASJC Scopus subject areas

  • General Health Professions


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