SPARSE REPRESENTATION FOR FACE RECOGNITION

Abstract

Sparse representation has attracted a great deal of attention in the past decade. Famous trans- !brrns such as discrete Fourier transform, wavelet transform and singular value decomposition are used to sparsely represent the signals. The aim of these transforms is to reveal certain structures of a signal and representation of these structures in a compact form. Therefore, sparse represen-tation provides high performance in the areas as diverse as image denoising, pattern classification, (olnpression etc. All of these applications are concerned with a compact and high-fidelity repre- sentation of signals. Iii this thesis, we consider the classical face recognition problem. This application is more conceriied with the semantic information of image signals. It is shown that a sparse representation based framework is a possible way to tackle this problem. We also propose a new approach for face classification which is based on task driven dictionary learning