3D Facial Expression Recognition Based on Primitive Surface Feature Distribution

By Jun Wang, Lijun Yin, Xiaozhou Wei, Yi Sun. Computer Vision and Pattern Recognition, 2006

350 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/2b4512097cb0056886f2d4d2ca7f5b034a647237

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

A 3D facial expression database for facial behavior research (2006, neutral): “The basic algorithm is outlined as follows: (details in the report [7]).”
Adaptive 3D Face Reconstruction from Unconstrained Photo Collections (2017, background): “Not only are faces one of the most commonly photographed objects, but having an understanding of the 3D face shape enables many applications in face recognition [2], 3D expression recognition [3], facial animations [4], avatar puppeteering [5], and more.”
Static and dynamic 3D facial expression recognition: A comprehensive survey (2012, methodology): “One of the main methods of classification that have been employed is Support Vector Machines (SVMs) [110,99,96,68,101,116,109,95,111], including multi-class SVMs [92,117].”
Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis (2016, background): “Researchers are already beginning to use 3D sensors and models to improve facial feature tracking and expression recognition [29, 34, 21, 25, 20, 33, 32].”
BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database (2014, methodology): “To further evaluate our approach, we conducted a comparison study by implementing the 3D static model-based approach using geometric primitive features [29] and the 2D texture-based approach using Gabor-wavelet features [31] to classify eight expressions from the entire BP4D-Spontaneous database.”
A high-resolution 3D dynamic facial expression database (2008, methodology): “For example, Yin et al. have investigated the 3D facial expression recognition [35] using 3D surface primitive features based on the 3D static facial expression database [40].”
Facial Expression Recognition by De-expression Residue Learning (2018, neutral): “[26] 3D 61.”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, methodology): “While some of the methods require manual labelling of fiducial vertices during training and testing [118], [131], [217], others are fully automatic [121], [124], [125], [133].”