Name
Abstract | ||
|---|---|---|
Expression recognition from non-frontal faces is a challenging research area with growing interest. This paper works with a generic sparse coding feature, inspired from object recognition, for multi-view facial expression recognition. Our extensive experiments on face images with seven pan angles and five tilt angles, rendered from the BU-3DFE database, achieve state-of-the-art results. We achieve a recognition rate of 69.1% on all images with four expression intensity levels, and a recognition performance of 76.1% on images with the strongest expression intensity. We then also present detailed analysis of the variations in expression recognition performance for various pose changes. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33885-4_58 | ECCV Workshops (3) |
Keywords | Field | DocType |
strongest expression intensity,object recognition,multi-view facial expression recognition,expression recognition performance,bu-3dfe database,generic sparse,recognition rate,challenging research area,expression intensity level,recognition performance,expression recognition | Computer vision,3D single-object recognition,Three-dimensional face recognition,Facial expression recognition,Pattern recognition,Neural coding,Computer science,Emotion recognition,Speech recognition,Feature (machine learning),Artificial intelligence,Cognitive neuroscience of visual object recognition | Conference |
Volume | ISSN | Citations |
7585 | 0302-9743 | 19 |
PageRank | References | Authors |
0.57 | 29 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Usman Tariq | 1 | 141 | 18.70 |
| jianchao yang | 2 | 7508 | 282.48 |
| Thomas S. Huang | 3 | 27815 | 2618.42 |