On Dimension Reduction Using Supervised Distance Preserving Projection for Face Recognition

Abstract

Personal identification or verification is a very common requirement in modern society specially to access restricted area or resources. Biometric identification specially faces identification or recognition in a controlled or an uncontrolled scenario has become one of the most important and challenging area of research. Images often are represented as high-dimensional vectors or arrays. Operating directly on these vectors would lead to high computational costs and storage demands. Also working directly with raw data is difficult, challenging or even impossible sometimes. Dimensionality reduction has become a necessity for pre-processing data, representation and classification. It aims to represent data in a low-dimensional space that captures the intrinsic nature of the data. In this article we have applied a Supervised distance preserving projection (SDPP) technique, Semidefinite Least Square SDPP (SLS-SDPP), we have proposed recently to reduce the dimension of face image data. Numerical experiments conducted on very well-known face image data sets both on gallery images and blurred images of various level demonstrate that the performance of SLS-SDPP is promising in comparison to two leading approach Eigenface and Fisherface.