Effect of Nonfaces in Reduction of Covariance Matrix Dimension in PCA for Face Recognition

Face recognition is a complex numerical technology in computer systems that can assist recognition of human faces using principal component analysis (PCA) by comparing the given facial uniqueness with the already available face database. Human faces are usually straight, so they can be treated as 2D images relatively rather than 3D. The 2D facial image can be transformed into a 1D vector of pixels and projected into the principal components of the feature space called the eigenspace projection, which is evaluated from the eigenvectors of the covariance matrix derived from a set of facial images. The projections of the given face are then compared with the available training set and the face is identified. In this paper, we introduce a novel approach of dimensionality reduction of the covariance matrix and apply this algorithm with the training sets of JNTU face database and nonface database. Recognition rate versus Euclidean distance and number of eigenfaces versus recognition rate and eigenvalue variation is presented in this paper.