A high-resolution 3D dynamic facial expression database
Abstract
Face information processing relies on the quality of data resource. From the data modality point of view, a face database can be 2D or 3D, and static or dynamic. From the task point of view, the data can be used for research of computer based automatic face recognition, face expression recognition, face detection, or cognitive and psychological investigation. With the advancement of 3D imaging technologies, 3D dynamic facial sequences (called 4D data) have been used for face information analysis. In this paper, we focus on the modality of 3D dynamic data for the task of facial expression recognition. We present a newly created high-resolution 3D dynamic facial expression database, which is made available to the scientific research community. The database contains 606 3D facial expression sequences captured from 101 subjects of various ethnic backgrounds. The database has been validated through our facial expression recognition experiment using an HMM based 3D spatio-temporal facial descriptor. It is expected that such a database shall be used to facilitate the facial expression analysis from a static 3D space to a dynamic 3D space, with a goal of scrutinizing facial behavior at a higher level of detail in a real 3D spatio-temporal domain.
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The most well known 3D datasets are BU-3DFE, Bosphorus, BU-4DFE and BP4D.
“The most well known 3D datasets are BU-3DFE [206], Bosphorus [195] (still images), BU-4DFE [207] (video) and BP4D [38] (video).”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [207]
BU-4DFE is a high-resolution 3D dynamic FE dataset with video sequences of 100 frames each from 101 subjects, containing only primary expressions.
“BU-4DFE is a high-resolution 3D dynamic FE dataset [207]. Video sequences, having 100 frames each, are captured from 101 subjects. It only contains primary expressions.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [207]
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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
Recognizing action units for facial expression analysis
Tian, Y.-I., Kanade, T., & Cohn, J. F. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908962 · Cited by 1,745 · see citing works
Affective multimodal human-computer interaction
Pantic, M., Sebe, N., Cohn, J. F., & Huang, T. (2005) · Proceedings of the 13th annual ACM international conference on Multimedia · doi:10.1145/1101149.1101299 · Cited by 234 · see citing works
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Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543262 · Cited by 4,303 · see citing works
BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database
Zhang, X., Yin, L., Cohn, J. F., Canavan, S., Reale, M., Horowitz, A., Liu, P., & Girard, J. M. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.06.002 · Cited by 745 · see citing works
Self-citationCorneanu, C. A., Simon, M. O., Cohn, J. F., & Guerrero, S. E. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2515606 · Cited by 527 · see citing works
Static and dynamic 3D facial expression recognition: A comprehensive survey
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Self-citationLearning Race from Face: A Survey
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Expression recognition from 3D dynamic faces using robust spatio-temporal shape features
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Expression Recognition Method Based on a Lightweight Convolutional Neural Network
Zhao, G., Yang, H., & Yu, M. (2020) · IEEE Access · doi:10.1109/access.2020.2964752 · Cited by 53 · see citing works
- Authors
- Yin, L., Chen, X., Sun, Y., Worm, T., & Reale, M.
- Affiliations
- Binghamton University (US)
- Venue
- 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition
- Publisher
- IEEE
- Published
- 2008-09
- Pages
- 1-6
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W2542323081
- Cited by
- 488
- References
- 3
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 6 · 2023: 11 · 2022: 21 · 2021: 32 · 2020: 51 · 2019: 47 · 2018: 44 · 2017: 34 · 2016: 48 · 2015: 37 · 2014: 35 · 2013: 32 · 2012: 30
- FWCI
- 14.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Automatic Face and Gesture Recognition (publishedVersion)
- Funders
- National Science Foundation
- Topics
- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- facial expression recognition, 3D face database, hidden Markov models
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