Name
Abstract | ||
|---|---|---|
Automatic facial expression recognition is the kernel part of emotional information processing. This paper dedicates to develop an automatic facial expression recognition approach based on a novel support vector machine tree, which performs feature selection at each internal node, to improve recognition accuracy and robustness. After the Pseudo-Zernike moment features were extracted, they were used to train a support vector machine tree for automatic recognition. The structure of a support vector machine enables the model to divide the facial recognition problem into sub-problems according to the teacher signals, so that it can solve the sub-problems in decreased complexity in different tree levels. In the training phase, those sub-samples assigned to two internal sibling nodes perform decreasing confusion cross, thus, the generalization ability for recognition of facial expression is enhanced. The compared results on Cohn-Kanade facial expression database also show that the proposed approach appeared higher recognition accuracy and robustness than other approaches. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72395-0_48 | ISNN (3) |
Keywords | Field | DocType |
support vector machine,recognition accuracy,automatic facial expression recognition,higher recognition accuracy,vector machine tree,automatic recognition,different tree level,cohn-kanade facial expression database,facial recognition problem,novel support vector machine,facial expression,facial expression recognition approach,novel support,feature selection,information processing | Facial recognition system,Face hallucination,Three-dimensional face recognition,Pattern recognition,Feature selection,Computer science,Support vector machine,Robustness (computer science),Facial expression,Feature (machine learning),Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
4493 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinzhen Xu | 1 | 10 | 3.57 |
| ZHANG Pin-zheng | 2 | 16 | 3.06 |
| Luxi Yang | 3 | 1180 | 118.08 |
| Wenjiang Pei | 4 | 49 | 17.26 |
| Zhenya He | 5 | 207 | 38.98 |