Classification with Repulsion Tensors: A Case Study on Face Recognition
This work addresses face recognition performance issues for computer vision applications, but it is incremental as it builds on existing repulsion strategies.
The paper tackles the problem of face recognition by extending a repulsion Laplacean technique to create repulsion energy between images from different classes in the projected space, resulting in significant recognition improvement relative to underlying two-dimensional methods.
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 "two-dimensional" 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 Laplacean' 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.