The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
A real-time automated system for the recognition of human facial expressions
Abstract
A fully automated, multistage system for real-time recognition of facial expression is presented. The system uses facial motion to characterize monochrome frontal views of facial expressions and is able to operate effectively in cluttered and dynamic scenes, recognizing the six emotions universally associated with unique facial expressions, namely happiness, sadness, disgust, surprise, fear, and anger. Faces are located using a spatial ratio template tracker algorithm. Optical flow of the face is subsequently determined using a real-time implementation of a robust gradient model. The expression recognition system then averages facial velocity information over identified regions of the face and cancels out rigid head motion by taking ratios of this averaged motion. The motion signatures produced are then classified using Support Vector Machines as either nonexpressive or as one of the six basic emotions. The completed system is demonstrated in two simple affective computing applications that respond in real-time to the facial expressions of the user, thereby providing the potential for improvements in the interaction between a computer user and technology.
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Cites
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Cited by
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.011 · Cited by 806
Emotional state classification from EEG data using machine learning approach
Wang, X.-W., Nie, D., & Lu, B.-L. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.06.046 · Cited by 755
Houssein, E. H., Hammad, A., & Ali, A. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07292-4 · Cited by 426
Face Expression Recognition and Analysis: The State of the Art
Bettadapura, V. (2012) · arXiv · doi:10.48550/arxiv.1203.6722 · Cited by 336
Metadata
- Authors
- Anderson, K., & McOwan, P. W.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
- Published
- 2006-02
- Volume
- 36
- Issue
- 1
- Pages
- 96-105
- ISSN
- 1083-4419 (ISSN-L)
- OpenAlex
- W2162418306
- Cited by
- 355
- References
- 30
- FWCI
- 17.33
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
