The DOI matches its Crossref record; 39 of the 41 references it cites were found in a registry or an index.
Recognizing action units for facial expression analysis
Open accessAbstract
Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions, such as happiness, anger, surprise, and fear. Such prototypic expressions, however, occur rather infrequently. Human emotions and intentions are more often communicated by changes in one or a few discrete facial features. In this paper, we develop an Automatic Face Analysis (AFA) system to analyze facial expressions based on both permanent facial features (brows, eyes, mouth) and transient facial features (deepening of facial furrows) in a nearly frontal-view face image sequence. The AFA system recognizes fine-grained changes in facial expression into action units (AUs) of the Facial Action Coding System (FACS), instead of a few prototypic expressions. Multistate face and facial component models are proposed for tracking and modeling the various facial features, including lips, eyes, brows, cheeks, and furrows. During tracking, detailed parametric descriptions of the facial features are extracted. With these parameters as the inputs, a group of action units (neutral expression, six upper face AUs and 10 lower face AUs) are recognized whether they occur alone or in combinations. The system has achieved average recognition rates of 96.4 percent (95.4 percent if neutral expressions are excluded) for upper face AUs and 96.7 percent (95.6 percent with neutral expressions excluded) for lower face AUs. The generalizability of the system has been tested by using independent image databases collected and FACS-coded for ground-truth by different research teams.
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“Based on the observation that some AU are better detected using geometrical features and others using appearance ones, it was suggested that a combination of both might increase recognition performance [127], [139], [183].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [139]
In the citing paper
“In the first part of the decade static representations were the primary choice in both RGB [99], [139], 3D [118], [120], [125], [131], [133], [219] and thermal [111].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [139]
In the citing paper
“In 2000, the CK dataset was published marking the beginning of modern AFER [139].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [139]
In the citing paper
“Others focused on appearance representations like Gabor filters [99], optical flow and LBPs [97] or combinations between the two [139].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [139]
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Cites
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Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Ekman, P. (1993) · American Psychologist · doi:10.1037/0003-066x.48.4.384 · Cited by 3,441
Comprehensive database for facial expression analysis
Takeo Kanade, A., Cohn, J. F., & Tian, Y. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2000.840611 · Cited by 2,644
Self-citationApplication of the Karhunen-Loeve procedure for the characterization of human faces
Kirby, M. J. L., & Sirovich, L. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.41390 · Cited by 2,459
Flagged first, then by citations
2 of the 41 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- eibl-eihesfeldt, 1989, Human Ethology
- ekman, 1978, The Facial Action Coding System A Technique for The Measurement of Facial Movement
- suwa, 1978, A Preliminary Note on Pattern Recognition of Human Emotional Expression, Proc Int'l Joint Conf Pattern Recognition, 408
- 2000, Facial Expression Coding Project
- 10.1109/34.506414 · DOI
Cited by
Sorted by citations
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Shan, C., Gong, S., & McOwan, P. W. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.005 · Cited by 2,300
Deep Facial Expression Recognition: A Survey
Li, S., & Deng, W. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2981446 · Cited by 2,175
Mitra, S., & Acharya, T. (2007) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.893280 · Cited by 2,048
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
Metadata
- Authors
- Tian, Y.-I., Kanade, T., & Cohn, J. F.
- Affiliations
- Carnegie Mellon University (US)
- University of Pittsburgh (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2001
- Volume
- 23
- Issue
- 2
- Pages
- 97-115
- ISSN
- 0162-8828 (ISSN-L)
- PMC
- PMC4157835
- OpenAlex
- W2106390385
- Cited by
- 1,745
- References
- 41
- Citations by year
- 2026: 34 · 2025: 56 · 2024: 71 · 2023: 70 · 2022: 106 · 2021: 100 · 2020: 78 · 2019: 94 · 2018: 63 · 2017: 70 · 2016: 80 · 2015: 72 · 2014: 85 · 2013: 80 · 2012: 67
- FWCI
- 52.28
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- University of Pittsburgh
- National Institute of Mental Health (R01MH051435)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression analysis, facial action units, facial action coding system, facial feature tracking, facial expression recognition, neutral expression
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