Expression recognition from time-sequential facial images by use of expression change model
Abstract
In order to construct a better human interface, recognition of facial expressions by means of computer is an important technology. An approach is proposed to recognize the degree of facial expression change from time-sequential images. The facial features in an input image sequence are tracked by using labeled graph matching with weighted links. To represent the relationship between the motion of features and change of expression, we construct expression change models by using B-spline curves. By making a comparison between the trajectory of features and the expression change models, the facial expression in the input image sequence can be recognized. Not only the category, but also the degree of facial expression change can be determined. Furthermore, the obtained expressional information is then fed back to guide the tracking in the next frame.
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The utilized techniques include holistic spatial analysis, spatio-temporal analysis, and analytic spatial analysis.
“The utilized techniques include holistic spatial analysis [13], [70], [54], spatio-temporal analysis [52], [100], [73], [57], and analytic spatial analysis [93], [43], [44], [92], [40].”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [13]
Cites
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Automatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778 · see citing works
- Authors
- Wang, M., Iwai, Y., & Yachida, M.
- Affiliations
- The University of Osaka (JP)
- Venue
- Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-27
- Pages
- 324-329
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W1897539390
- Cited by
- 60
- References
- 9
- Citations by year
- 2026: 1 · 2019: 1 · 2018: 2 · 2017: 1 · 2016: 1 · 2015: 5 · 2014: 6 · 2013: 2 · 2012: 9
- FWCI
- 2.61
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Automatic Face and Gesture Recognition (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression recognition, facial feature tracking, B-spline curves, feature trajectories, human-machine interaction
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