Name
Abstract | ||
|---|---|---|
Automated recognition of facial expression is an important addition to computer vision research because of its relevance to the study of psychological phenomena and the development of human-computer interaction (HCI). We developed a computer vision system that automatically recognizes individual action units or action unit combinations in the upper face using Hidden Markov Models (HMMs). Our approach to facial expression recognition is based on the Facial Action Coding System (FACS), which separates expressions into upper and lower face action. In this paper, we use three approaches to extract facial expression information: (I) facial feature point tracking, (2) dense flow tracking with principal component analysis (PCA), and (3) high gradient component detection (i.e., furrow detection). The recognition results of the upper face expressions using feature point tracking, dense pow tracking, and high gradient component detection are 85%, 93% and 85% respectively. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/AFGR.1998.670980 | FG |
Keywords | Field | DocType |
feature extraction,facial expression,image recognition,hidden markov model,hidden markov models,neutron spin echo,principal component analysis,facial action coding system,user interfaces,face detection,face recognition,computer vision,psychology,human computer interaction | Facial recognition system,Computer vision,Facial Action Coding System,Pattern recognition,Expression (mathematics),Computer science,Feature extraction,Facial expression,Artificial intelligence,Face detection,Hidden Markov model,Principal component analysis | Conference |
ISBN | Citations | PageRank |
0-8186-8344-9 | 69 | 26.68 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Jenn-Jier Lien | 1 | 184 | 35.29 |
| Takeo Kanade | 2 | 25073 | 4203.02 |
| Jeffrey F. Cohn | 3 | 5438 | 343.74 |
| Ching-chung Li | 4 | 383 | 65.47 |