Name
Abstract | ||
|---|---|---|
In this paper, we introduce a new facial-expression analysis system designed to automatically recognize facial expressions, able to manage facial-expression intensity variation as well as reducing the doubt and confusion between facial-expression classes. Our proposed approach introduces a new method to segment efficiently facial feature contours using Vector Field Convolution (VFC) technique. Relying on the detected contours, we extract facial feature points which go with facial-expression deformations. Then we have modeled a set of distances among the detected points to define prediction rules through data mining technique. An experimental study was conducted to evaluate the performance of our proposed solution under varying factors. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.3233/978-1-61499-330-8-185 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Facial-expressions recognition,Vector Field Convolution,Data Mining,Facial features detection,Facial features segmentation | Data mining,Computer vision,Confusion,Recognition system,Computer science,Vector field convolution,Facial expression,Artificial intelligence | Conference |
Volume | ISSN | Citations |
257 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mliki Hazar | 1 | 11 | 4.91 |
| Nesrine Fourati | 2 | 26 | 4.74 |
| Mohamed Hammami | 3 | 181 | 30.54 |
| Hanêne Ben-Abdallah | 4 | 398 | 71.57 |
| Saudi Arabia | 5 | 84 | 12.67 |