Name
Abstract | ||
|---|---|---|
One of the challenging problems encountered by face recognition is the difficulty in tackling pose variations, where fast and reliable head pose estimation is an essential step. In this paper, based on the correlation filter technique, a novel feature extraction framework, i.e., directional correlation filter set (DCFS), is developed for robust head pose estimation. In this framework, a principal optimal tradeoff filter (called POTF) is designed in the feature subspace obtained according to principal component analysis (PCA). Compared with the traditional methods that rely on the exact localization of facial feature points, our proposed method exploits the 1D frequency domain of the training data by using the correlation filter technique, which can capture the high-order statistics of a face for effective head pose estimation. Experimental results on several public face databases with large pose variations, including PIE, HPI, and UMIST, show the promising performance obtained by the proposed method on head pose estimation. © 2013 Springer-Verlag Berlin Heidelberg. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-42057-3-41 | IScIDE |
Keywords | Field | DocType |
correlation filter,feature extraction,head pose estimation | Frequency domain,Training set,Facial recognition system,Data mining,Correlation filter,Subspace topology,Pattern recognition,Computer science,Feature extraction,Pose,Artificial intelligence,Principal component analysis | Conference |
Volume | Issue | ISSN |
8261 LNCS | null | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Yan | 1 | 17 | 6.40 |
| Yan Yan | 2 | 240 | 48.08 |
| Hanzi Wang | 3 | 1107 | 92.85 |