Facial expression analysis by machine learning
Cho, Siu-Yeung, Teoh, Teik-Toe, and Nguwi, Yok-Yen (2011) Facial expression analysis by machine learning. In: Zhang, Yu-Jin, (ed.) Advances in face image analysis : techniques and technologies. Medical Information Science Reference, Hershey, PA, USA, pp. 239-258.
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Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face which 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 sometime conventional classifiers are not even effective to handle those features and then 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.
|Item Type:||Book Chapter (Research - B1)|
|Date Deposited:||02 Oct 2012 05:21|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems @ 100%|
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890202 Application Tools and System Utilities @ 100%|
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