Fully Automatic Facial Action Unit Detection and Temporal Analysis

By Michel François Valstar, Maja Pantic. 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06), 2006

386 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/f39d53555872d83f42ac2385e2ae8e4f5e2d0282

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

1. Maja Pantic
2. Oksam Chae
3. Shaogang Gong
4. Luc Van Gool
5. Xiaogang Wang
6. Jeffrey F. Cohn
7. Fernando De la Torre
8. Yi Yang

In Experimental and Cognitive Psychology

1. Maja Pantic
2. Oksam Chae
3. Peter W. McOwan
4. Caifeng Shan
5. Shaogang Gong
6. Jeffrey F. Cohn
7. Fernando De la Torre
8. Md. Hasanul Kabir

Citations in the corpus

• Facial expression recognition based on Local Binary Patterns: A comprehensive study (2009, methodology): “Recently they [23] further presented a fully automatic AU detection system that can automatically localize facial points in the first frame and recognize AU temporal segments using a subset of most informative spatio-temporal features selected by AdaBoost.”
• Robust Facial Expression Recognition Based on Local Directional Pattern (2010, background): “Valstar and others [15], [16] presented detection by classifying features calculated from tracked fiducial facial points and urged that geometric approaches have similar or better performance than appearance-based approaches in facial expression analysis.”
• Social signal processing: Survey of an emerging domain (2009, background): “Outline of a geometric-feature-based system for detection of facial AUs and their temporal phases (onset, apex, offset, neutral) proposed in [196].”
• Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark (2011, neutral): “Furthermore, facial expression analysis methods can also be classified into image based [18, 13] and video based [23, 28].”
• Human computing and machine understanding of human behavior: a survey (2006, neutral): “A feature-based facial action detection system [61].”
• Exploiting sparsity and co-occurrence structure for action unit recognition (2015, methodology): “Previous work on AU detection from video includes Valstar and Pantic [27], who demonstrate high agreement with human coders on 15 AUs, and Bartlett et al. [4] who use a framework combining Gabor features and SVMs to detect 17 AUs.”
• Facial Expression Analysis (2011, background): “Valstar and Pantic [121] used a combination of SVM and HMM to temporally segment and recognize AUs.”