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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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Zhou, J., Zhang, X., Lin, Y., & Liu, Y. M. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.103018 · Cited by 6
Kondo, K., Nakamura, T., Nakamura, Y., & Satoh, S. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9411988 · Cited by 6
Combating Uncertainty and Class Imbalance in Facial Expression Recognition
Fan, J., Zhou, J. W., Deng, X., Wang, H., Tao, L., & Kwan, H. K. (2022) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon55691.2022.9977693 · Cited by 6
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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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