Name
Abstract | ||
|---|---|---|
In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of L-1-norm regularization in the LPP objective fwiction, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Elcman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1587/transfun.E97.A.1650 | IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES |
Keywords | Field | DocType |
Facial expression recognition, Sparse locality preserving projection (SLPP), Feature selection | Locality,Pattern recognition,Facial expression recognition,Feature selection,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
E97A | 7 | 0916-8508 |
Citations | PageRank | References |
1 | 0.35 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingjie Yan | 1 | 13 | 1.68 |
| Wenming Zheng | 2 | 1240 | 80.70 |
| Minghai Xin | 3 | 55 | 5.70 |
| Jingwei Yan | 4 | 68 | 3.44 |