Classification with Repulsion Tensors: A Case Study on Face Recognition

TL;DR

This paper introduces a repulsion tensor technique to improve face recognition by ensuring dissimilar images are well separated in the reduced feature space, enhancing recognition accuracy.

Contribution

It extends repulsion-based dimensionality reduction methods to a tensor framework, improving face recognition performance over traditional two-dimensional approaches.

Findings

Significant recognition accuracy improvements achieved.

The method effectively separates dissimilar images in the reduced space.

Applicable to various two-dimensional face recognition methods.

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 "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.