The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Classifying facial actions
Open accessAbstract
The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions.
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A specific neural substrate for perceiving facial expressions of disgust
Phillips, M. L., Young, A. W., Senior, C., Brammer, M., Andrew, C., Calder, A. J., Bullmore, E. T., Perrett, D. I., Rowland, D., Williams, S. C. R., et al. (1997) · Nature · doi:10.1038/39051 · Cited by 1,609
Measuring facial expressions by computer image analysis
Bartlett, M. S., Hager, J. C., Ekman, P., & Sejnowski, T. J. (1999) · Psychophysiology · doi:10.1017/s0048577299971664 · Cited by 424
Self-citation
Cited by
Detecting faces in images: a survey
Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,427
Toward machine emotional intelligence: analysis of affective physiological state
Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Shan, C., Gong, S., & McOwan, P. W. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.005 · Cited by 2,300
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893
Automatic facial expression analysis: a survey
Fasel, B., & Luettin, J. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(02)00052-3 · Cited by 1,810
Metadata
- Authors
- Donato, G., Bartlett, M. S., Hager, J. C., Ekman, P., & Sejnowski, T. J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1999
- Volume
- 21
- Issue
- 10
- Pages
- 974-989
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2133180260
- Cited by
- 999
- References
- 5
- FWCI
- 31.23
- Type
- journal article
- Language
- en
- Open access
- green
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