Pose & Size Independent Face Recognition Using Advanced PCA Algorithm

Abstract

This paper mainly addresses the building of not only pose but also size independent face recognition system by using Principal Component Analysis (PCA). Furthermore, This is an illustration of face recognition method including both algorithm as well as schematic diagram where the dimension of testing image need not to be equal with the dimension of database or training images. PCA is a statistical approach used for reducing the number of variables in face recognition. In PCA, every image in the training set is represented as a linear combination of weighted eigenvectors called eigenfaces. These eigenvectors are obtained from covariance matrix of a training image set. The weights are found out after selecting a set of most relevant Eigenfaces. Recognition is performed by projecting a test image onto the subspace spanned by the eigenfaces and then classification is done by measuring minimum Euclidean distance. A number of experiments were done to evaluate the performance of the face recognition system. In this thesis, we used a training database of students of Electronics and Telecommunication Engineering department, Batch-2007, Rajshahi University of Engineering and Technology, Bangladesh.