COMPARATIVE ANALYSIS OF FACE RECOGNITION BASED ON SIGNIFICANT PRINCIPAL COMPONENTS OF PCA TECHNIQUE

Abstract

Face recognition systems have been emerging as acceptable approaches for human authorization. Face recognition help in searching and classifying a face database and at a higher level help in identification of possible threats to security. In face recognition problem, the objective is to search a face in the reference face database that matches a given subject. The task of face recognition involves the extraction of feature vectors of the human face from the face image for differentiating it from other persons. In this work, the comparative analysis is done based on the varying number of highly significant principal components (Eigenvectors) of PCA for face recognition. Experimental results show a small number of principal components of PCA are required for matching. PCA technique is a statistical technique, it reduces the dimension of the search space that best describes the images.