ResearchBib Share Your Research, Maximize Your Social Impacts
Sign for Notice Everyday Sign up >> Login

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

Journal: International Journal of Computer Engineering and Technology (IJCET) (Vol.10, No. 1)

Publication Date:

Authors : ;

Page : 94-101

Keywords : Covariance Matrix; Eigenvector; Eigenvalue; Euclidean Distance; ORL; PCA.;

Source : Downloadexternal Find it from : Google Scholarexternal

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 [6]. 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.

Last modified: 2019-03-05 22:29:41