A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

Share/Save/Bookmark

Zeng, Zhihong and Pantic, Maja and Roisman, Glenn I. and Huang, Thomas S. (2009) A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions. IEEE transactions on pattern analysis and machine intelligence, 31 (1). pp. 39-58. ISSN 0162-8828

[img] PDF
Restricted to UT campus only
: Request a copy
4MB
Abstract:Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions, despite the fact that deliberate behavior differs in visual appearance, audio profile, and timing from spontaneously occurring behavior. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behavior have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis, including audiovisual fusion, linguistic and paralinguistic fusion, and multicue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next, we examine available approaches for solving the problem of machine understanding of human affective behavior and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.
Item Type:Article
Copyright:© 2009 IEEE
Faculty:
Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:
Link to this item:http://purl.utwente.nl/publications/62678
Official URL:http://dx.doi.org/10.1109/TPAMI.2008.52
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page

Metis ID: 263713