Name
Abstract | ||
|---|---|---|
Under the condition of multi-databases, a novel algorithm of facial expression recognition was proposed to improve the robustness of traditional semi-supervised methods dealing with individual differences in facial expression recognition. First, the regions of interest of facial expression images were determined by face detection and facial expression features were extracted using Linear Discriminant Analysis. Then Transfer Learning Adaptive Boosting (TrAdaBoost) algorithm was improved as semi-supervised learning method for multi-classification. The results show that the proposed method has stronger robustness than the traditional methods, and improves the facial expression recognition rate from multiple databases. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-02750-0_14 | AMT |
Keywords | Field | DocType |
facial expression recognition,semi-supervised learning,tradaboost | Semi-supervised learning,Computer science,Transfer of learning,Robustness (computer science),Artificial intelligence,Face detection,Computer vision,Pattern recognition,Three-dimensional face recognition,Algorithm,Facial expression,Boosting (machine learning),Linear discriminant analysis | Conference |
Volume | Issue | ISSN |
8210 LNCS | null | 16113349 |
Citations | PageRank | References |
1 | 0.35 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Jiang | 1 | 1 | 2.38 |
| Ke-Bin Jia | 2 | 126 | 44.30 |
| Zhonghua Sun | 3 | 75 | 26.21 |