The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Facial expression recognition using a dynamic model and motion energy
Abstract
Previous efforts at facial expression recognition have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static facial "mugshots." We develop new more accurate representations for facial expression by building a video database of facial expressions and then probabilistically characterizing the facial muscle activation associated with each expression using a detailed physical model of the skin and muscles. This produces a muscle based representation of facial motion, which is then used to recognize facial expressions in two different ways. The first method uses the physics based model directly, by recognizing expressions through comparison of estimated muscle activations. The second method uses the physics based model to generate spatio temporal motion energy templates of the whole face for each different expression. These simple, biologically plausible motion energy "templates" are then used for recognition. Both methods show substantially greater accuracy at expression recognition than has been previously achieved.>
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Cites
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Facial motion in the perception of faces and of emotional expression.
Bassili, J. N. (1978) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/0096-1523.4.3.373 · Cited by 369
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
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
Kotsia, I., & Pitas, I. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.884954 · Cited by 687
Facial emotion recognition using multi-modal information
De Silva, L. C., Miyasato, T., & Nakatsu, R. (2002) · Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat. No.97TH8237) · doi:10.1109/icics.1997.647126 · Cited by 319
Communication via eye blinks and eyebrow raises: video-based human-computer interfaces
Grauman, K., Betke, M., Lombardi, J., Gips, J., & Bradski, G. R. (2003) · Universal Access in the Information Society · doi:10.1007/s10209-003-0062-x · Cited by 185
Metadata
- Authors
- Essa, I. A., & Pentland, A. P.
- Venue
- Proceedings of IEEE International Conference on Computer Vision
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-11-19
- Pages
- 360-367
- OpenAlex
- W2098790470
- Cited by
- 251
- References
- 2
- FWCI
- 18.50
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Essa, I. A., & Pentland, A. P. (2002). Facial expression recognition using a dynamic model and motion energy. Proceedings of IEEE International Conference on Computer Vision. https://doi.org/10.1109/iccv.1995.466916
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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