Spotting segments displaying facial expression from image sequences using HMM
Abstract
A method that can be used for spotting segments that display facial expression is proposed. The motion of the face is modeled by HMM in such a way that each state corresponds to the conditions of facial muscles, e.g., relaxed, contracting, apex and relaxing. The probability assigned to each state is updated iteratively as the feature vector is obtained from image processing. A spotted segment is placed into a certain category when the probability of that category exceeds a threshold value. Experiments show that the segments for the six basic expressions can be spotted accurately in near real time.
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Three types of face representation are mainly used in facial expression analysis: holistic, analytic, and hybrid.
“In general, three types of face representation are mainly used in facial expression analysis: holistic (e.g., isodensity maps [38]), analytic (e.g., deformable templates [105]), and hybrid (e.g., analytic-to-holistic approach [45]).”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [105]
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References 15 · Cited by 69
- Authors
- Otsuka, T., & Ohya, J.
- Affiliations
- Advanced Telecommunications Research Institute International (JP)
- ORCID
- Otsuka, T.
- Venue
- Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-27
- Pages
- 442-447
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W1602325643
- Cited by
- 69
- References
- 15
- Citations by year
- 2021: 1 · 2020: 3 · 2018: 4 · 2017: 1 · 2016: 4 · 2015: 2 · 2014: 4 · 2013: 3 · 2012: 4
- FWCI
- 3.01
- 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, hidden Markov models, image sequences, feature vectors, real-time detection, basic emotions
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