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Facial Expression Recognition

  • Yingli Tian
  • Takeo Kanade
  • Jeffrey F. Cohn

Abstract

This chapter introduces recent advances in facial expression analysis and recognition. The first part discusses general structure of AFEA systems. The second part describes the problem space for facial expression analysis. This space includes multiple dimensions: level of description, individual differences in subjects, transitions among expressions, intensity of facial expression, deliberate versus spontaneous expression, head orientation and scene complexity, image acquisition and resolution, reliability of ground truth, databases, and the relation to other facial behaviors or nonfacial behaviors. We note that most work to date has been confined to a relatively restricted region of this space. The last part of this chapter is devoted to a description of more specific approaches and the techniques used in recent advances. They include the techniques for face acquisition, facial data extraction and representation, facial expression recognition, and multimodal expression analysis. The chapter concludes with a discussion assessing the current status, future possibilities, and open questions about automatic facial expression analysis.

Keywords

Facial Expression Face Image Facial Feature Expression Recognition Facial Expression Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We sincerely thank Zhen Wen and Hatice Gunes for providing pictures and their permission to use them in this chapter.

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© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Electrical EngineeringThe City College of New YorkNew YorkUSA
  2. 2.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  3. 3.Department of PsychologyUniversity of PittsburghPittsburghUSA

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