Face recognition using PCA based algorithm and neural network

Abstract

In this paper we develop a computational model to identify the unknown person's face by comparing characteristics of face to those of known individuals. Principal Component Analysis, based on information theory concepts, seek a computational model that best describe a face. Eigenface approach is the principal component analysis method, in which small set of characteristic pictures are used to describe the variation between face images. Goal is to find out the eigenvectors (eigenfaces) of the covariance matrix of the distribution, spanned by a training set of face images. Later, every face image is represented by a linear combination of these eigenvectors. The eigenface algorithm has been applied to extract the basic face of the human face images stored in database of faces (e.g. ORL face database). Recognition is performed by projecting a new image into the subspace spanned by the eigenfaces and then classifying the face by comparing its position in face space with the positions of known individuals. In this approach we treat the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by 2-D characteristic views.