A 3D facial expression database for facial behavior research

By Lijun Yin, Xiaozhou Wei, Yi Sun, Jun Wang, Matthew J. Rosato. 2006

1,412 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

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In Experimental and Cognitive Psychology

Citations in the corpus

Generating 3D faces using Convolutional Mesh Autoencoders (2018, background): “Existing large scale datasets [14, 16, 50, 49, 38] do not contain high resolution extreme facial expressions.”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “6 BU-3DFE [75] 2006 2,500 3D images 100 SBE”
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition (2015, background): “from naturalistic environments with spontaneous affective behaviour [118] as existing datasets [122], [138], [176] contain exaggeratedly posed affective behaviour.”
The Vera am Mittag German audio-visual emotional speech database (2008, background): “Databases with affective facial expressions are summarized in [10].”
Automatic Recognition of Facial Actions in Spontaneous Expressions (2006, background): “Two more recent databases also make a major contribution to the field: The MMI database which enables greater temporal analysis as well as profile views [38], as well as the Lin database which contains 3D range data for prototypical expressions at a variety of intensities [30].”
Deep Facial Expression Recognition: A Survey (2018, background): “The Binghamton University 3D Facial Expression (BU-3DFE) database contains 606 facial expression sequences captured from 100 people.”
DISFA: A Spontaneous Facial Action Intensity Database (2013, neutral): “3DFE face [52] and Bosphorus database [51]).”
Nonlinear 3D Face Morphable Model (2018, background): “Lack of expression can be compensated using expression bases from FaceWarehouse [9] or BD-3FE [43].”
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation (2017, methodology): “For a quantitative comparison, we used the first 200 subjects from the BU-3DFE dataset [45], which contains facial images aligned with ground truth depth images.”
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution (2006, background): “A detail description of the database construction, post-processing, and organization can be found in [29].”
CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation (2014, neutral): “D facial expression databases [18,19].”
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network (2018, result): “We also provide additional quantitative results on BU-3DFE[62] and qualitative results on 300VW[11] and Multi-PIE[23] datasets, please refer to supplementary material for full details.”
The MUG facial expression database (2010, background): “It includes 100 subjects, who performed the six expressions in front of a 3D scanner.”
ASCERTAIN: Emotion and Personality Recognition Using Commercial Sensors (2018, methodology): “modalities employed for AR include audio [14], [15], [16], visual [17], [18], [19] and audio-visual [20], [21], [22].”
3D Morphable Face Models—Past, Present, and Future (2019, positive): “Currently, many approaches [Feng et al. 2018; Kim et al. 2018b; Klaudiny et al. 2017; McDonagh et al. 2016; Richardson et al. 2016; Sela et al. 2017; Yu et al. 2017] in the research community are trained on synthetic training data, since it is easy to acquire and comes by design with perfect…”