Facial expression recognition from video sequences: temporal and static modeling
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Most early works used geometric representations, like vectors, active contours, or deformable 2D mesh models.
“Most of these early works used geometric representations, like vectors for describing the motion of the face [134], active contours for describing the shape of the mouth and eyebrows [137], or deformable 2D mesh models [116].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [134]
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“Darwin [7] held an ethological view of emotional expressions , arguing that the expressions from infancy and lower life forms exist in adult humans.”
BackgroundFacial emotion recognition using multi-modal information
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Facial expression recognition based on Local Binary Patterns: A comprehensive study
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Emotion Recognition Based on Joint Visual and Audio Cues
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A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data
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Self-citationReal-time 2D+3D facial action and expression recognition
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Error weighted semi-coupled hidden Markov model for audio-visual emotion recognition
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Facial expression recognition via deep learning
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Dynamic facial expression recognition by joint static and multi-time gap transition classification
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- Authors
- Cohen, I., Sebe, N., Garg, A., Chen, L. S., & Huang, T. S.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- University of Amsterdam (NL)
- IBM (United States) (US)
- Carestream (United States) (US)
- Corresponding author
- Cohen, Ira
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2003-07
- Volume
- 91
- Issue
- 1-2
- Pages
- 160-187
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2154716422
- Cited by
- 874
- References
- 3
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 11 · 2023: 16 · 2022: 38 · 2021: 32 · 2020: 23 · 2019: 35 · 2018: 38 · 2017: 41 · 2016: 45 · 2015: 60 · 2014: 62 · 2013: 61 · 2012: 55
- FWCI
- 42.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Vision and Image Understanding (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- Iris (University of Trento) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression recognition, video sequences, Bayesian network classifiers, naive Bayes, Cauchy distribution, tree augmented naive Bayes, hidden Markov models, temporal cues, user-independent recognition, emotion recognition
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