Static and dynamic 3D facial expression recognition: A comprehensive survey

By Georgia Sandbach, Stefanos P. Zafeiriou, Maja Pantic, Lijun Yin. Image and Vision Computing, 2012

393 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/31bb49ba7df94b88add9e3c2db72a4a98927bb05

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In Human-Computer Interaction

Citations in the corpus

Modeling Stylized Character Expressions via Deep Learning (2016, methodology): “The FACS system is often used as a basis for designing character animation systems [5,9] and for facial expression recognition on scanned 3D faces [10].”
Automatic Analysis of Facial Actions: A Survey (2019, background): “However, we limit the scope of this survey to 2D, and refer the reader to [151], [185] for an overview”
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].”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, background): “In the last years excellent surveys on automatic facial expression analysis have been published [9], [10], [11], [12].”
A Brief Review of Facial Emotion Recognition Based on Visual Information (2018, background): “The LSTM or RNN model for modeling sequential images has two advantages compared to An LSTM has the ability to remove or add information to the cell state. standalone approaches.”
BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database (2014, background): “A number of 2D facial expression databases have become available (e.g., [1] [2] [7] [8] [16]), as well as some with 3D imaging data (e.g., [9] [14] [15] [24] [25] [45]).”
EMOCA: Emotion Driven Monocular Face Capture and Animation (2022, background): “While early work like Wen and Huang [94] uses 3D non-rigid surface tracking to extract features for expression reconstruction, the majority of 3D-based methods focus on recognizing expressions from 3D scans [58, 72].”
State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications (2018, background): “Finally, driven by applications in security and person identiﬁcation, there is a large body of work on face recognition [ZCPR03, ANRS07], and more speciﬁcally expression recognition [SZPY12], which aims to advance user experiences in human-computer interaction scenarios.”
Facial Expression Analysis under Partial Occlusion (2018, methodology): “Although FEA using 3D data has been researched intensively (see [Sandbach, et al. 2012] for a recent survey on existing studies), the approaches that are specifically designed to overcome facial occlusion are still limited and largely focus on handling self-occlusion caused by head pose variations.”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, background): “Interesting work can be found in related survey papers [212], [273], [274].”
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.”