Name
Abstract | ||
|---|---|---|
Machine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This paper explores whether current machine learning methods can be used to develop an expression recognition system that operates reliably in more realistic conditions. We explore the necessary characteristics of the training data set, image registration, feature representation, and machine learning algorithms. A new database, GENKI, is presented which contains pictures, photographed by the subjects themselves, from thousands of different people in many different real-world imaging conditions. Results suggest that human-level expression recognition accuracy in real-life illumination conditions is achievable with machine learning technology. However, the data sets currently used in the automatic expression recognition literature to evaluate progress may be overly constrained and could potentially lead research into locally optimal algorithmic solutions. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TPAMI.2009.42 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
current machine,controlled lighting condition,facial expression recognition,practical smile detection,automatic facial expression recognition,expression recognition system,different real-world imaging condition,different people,automatic expression recognition literature,human-level expression recognition accuracy,training data,computer vision,gesture recognition,automatic control,image registration,indexing terms,machine learning,learning artificial intelligence,facial expression,abstract machine,face recognition | Computer science,Gesture recognition,Feature (machine learning),Artificial intelligence,Computer vision,Object detection,Facial recognition system,Three-dimensional face recognition,Pattern recognition,Automatic control,Facial expression,Machine learning,Image registration | Journal |
Volume | Issue | ISSN |
31 | 11 | 1939-3539 |
Citations | PageRank | References |
92 | 5.43 | 33 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacob Whitehill | 1 | 988 | 58.75 |
| gwen littlewort | 2 | 1159 | 67.40 |
| Ian Fasel | 3 | 431 | 31.58 |
| Marian Stewart Bartlett | 4 | 223 | 11.41 |
| Javier R. Movellan | 5 | 281 | 18.37 |