Optimized Projection for Sparse Representation Based Classification

TL;DR

This paper introduces OP-SRC, a supervised dimensionality reduction technique tailored for sparse representation based classification, enhancing face recognition accuracy by optimizing class-specific residuals.

Contribution

The paper proposes a novel supervised DR method, OP-SRC, specifically designed to improve SRC-based classification by optimizing projection to reduce within-class residuals and increase between-class residuals.

Findings

OP-SRC improves face recognition accuracy on Yale, ORL, and UMIST datasets.

The method effectively reduces within-class residuals and increases between-class residuals.

Experimental results demonstrate the superiority of OP-SRC over existing methods.

Abstract

Dimensionality reduction (DR) methods have been commonly used as a principled way to understand the high-dimensional data such as facial images. In this paper, we propose a new supervised DR method called Optimized Projection for Sparse Representation based Classification (OP-SRC), which is based on the recent face recognition method, Sparse Representation based Classification (SRC). SRC seeks a sparse linear combination on all the training data for a given query image, and make the decision by the minimal reconstruction residual. OP-SRC is designed on the decision rule of SRC, it aims to reduce the within-class reconstruction residual and simultaneously increase the between-class reconstruction residual on the training data. The projections are optimized and match well with the mechanism of SRC. Therefore, SRC performs well in the OP-SRC transformed space. The feasibility and effectiveness of the proposed method is verified on the Yale, ORL and UMIST databases with promising results.