Name
Abstract | ||
|---|---|---|
We consider dimensionality reduction methods for face recognition in a supervised setting, using an image-as-matrix representation. A common procedure is to project image matrices into a smaller space in which the recognition is performed. These methods are often called in the literature and there exist counterparts that use an image-as-vector representation. When two face images are close to each other in the input space they may remain close after projection - but this is not desirable in the situation when these two images are from different classes, and this often affects the recognition performance. We extend a previously developed `repulsion Laplaceanu0027 technique based on adding terms to the objective function with the goal or creation a repulsion energy between such images in the projected space. This scheme, which relies on a repulsion graph, is generic and can be incorporated into various two-dimensional methods. It can be regarded as a multilinear generalization of the repulsion strategy by Kokiopoulou and Saad [Pattern Recog., 42 (2009), pp. 2392--2402]. Experimental results demonstrate that the proposed methodology offers significant recognition improvement relative to the underlying two-dimensional methods. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Graph,Facial recognition system,Dimensionality reduction,Tensor,Pattern recognition,Matrix (mathematics),Computer science,Artificial intelligence,Multilinear map,Machine learning |
DocType | Volume | Citations |
Journal | abs/1603.04588 | 0 |
PageRank | References | Authors |
0.34 | 11 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haw-ren Fang | 1 | 132 | 13.24 |