Name
Abstract | ||
|---|---|---|
This paper proposes a new image feature extraction method for face recognition, called dual-kernel based two dimensional linear discriminant analysis (D-K2DLDA), by integrating multiple kernel discriminant analysis with the existing K2DFDA method. The proposed method deals with a face image directly as a matrix, instead of a stacked vector from rows or columns of the image. Moreover, we separately perform an iterative scheme for kernel parameter optimization for each of the two kernels, based on the maximum margin criterion and the damped Newton’s method, followed by a fusion procedure of the two kernels. Experimental results on the ORL and UMIST face databases show the effectiveness of D-K2DLDA. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s12652-014-0230-2 | Journal of Ambient Intelligence and Humanized Computing |
Keywords | Field | DocType |
Multiple kernel, Linear discriminant analysis, Matrix representation, Face recognition | Optimal discriminant analysis,Kernel (linear algebra),Facial recognition system,Pattern recognition,Computer science,Matrix (mathematics),Multiple discriminant analysis,Kernel Fisher discriminant analysis,Feature extraction,Artificial intelligence,Linear discriminant analysis,Machine learning | Journal |
Volume | Issue | ISSN |
6 | 5 | 1868-5145 |
Citations | PageRank | References |
3 | 0.38 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-Zhang Liu | 1 | 12 | 3.61 |
| Hongwei Ye | 2 | 3 | 0.38 |