A 3D Facial Expression Database For Facial Behavior Research
Abstract
Traditionally, human facial expressions have been studied using either 2D static images or 2D video sequences. The 2D-based analysis is incapable of handing large pose variations. Although 3D modeling techniques have been extensively used for 3D face recognition and 3D face animation, barely any research on 3D facial expression recognition using 3D range data has been reported. A primary factor for preventing such research is the lack of a publicly available 3D facial expression database. In this paper, we present a newly developed 3D facial expression database, which includes both prototypical 3D facial expression shapes and 2D facial textures of 2,500 models from 100 subjects. This is the first attempt at making a 3D facial expression database available for the research community, with the ultimate goal of fostering the research on affective computing and increasing the general understanding of facial behavior and the fine 3D structure inherent in human facial expressions. The new database can be a valuable resource for algorithm assessment, comparison and evaluation
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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 [206]
In BU-3DFE, 6 expressions from 100 different subjects are captured on four different intensity levels.
“In BU-3DFE, 6 expression from 100 different subjects are captured on four different intensity levels.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [206]
The publication of the BU-3DFE dataset was a starting point for consistently extending RGB FE recognition to 3D.
“The publication of the BU-3DFE dataset [206] was a starting point for consistently extending RGB FE recognition to 3D.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [206]
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Cites
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- Authors
- Yin, L., Wei, X., Sun, Y., Wang, J., & Rosato, M. J.
- Affiliations
- Binghamton University (US)
- Venue
- 7th International Conference on Automatic Face and Gesture Recognition (FGR06)
- Publisher
- IEEE
- Published
- 2006-04-28
- Pages
- 211-216
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W2137306662
- Cited by
- 1,171
- References
- 4
- Citations by year
- 2026: 19 · 2025: 25 · 2024: 49 · 2023: 49 · 2022: 52 · 2021: 102 · 2020: 86 · 2019: 105 · 2018: 89 · 2017: 64 · 2016: 68 · 2015: 71 · 2014: 66 · 2013: 73 · 2012: 57
- FWCI
- 25.77
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Automatic Face and Gesture Recognition (publishedVersion)
- Funders
- National Science Foundation (0414029, 0541044)
- 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
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Keywords
- facial expression recognition, 3D range data, facial behavior, affective computing, 3D face models, facial texture, pose variation
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