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: 2019-01-31
Authors : Manzoor Ahmad Lone;
Page : 94-101
Keywords : Covariance Matrix; Eigenvector; Eigenvalue; Euclidean Distance; ORL; PCA.;
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.
Other Latest Articles
- AN IMPLEMENTATION OF SOFTWARE EFFORT DURATION AND COST ESTIMATION WITH STATISTICAL AND MACHINE LEARNING APPROACHES
- EXPERIMENTAL STUDY ON CLOUD SECURITY FOR PERSONAL HEALTH RECORDS OVER PATIENT CENTRIC DATA
- UNDERSTANDING ADOPTION FACTORS OF OVER-THE-TOP VIDEO SERVICES AMONG MILLENNIAL CONSUMERS
- A COMPARATIVE STUDY ON GOOGLE APP ENGINE AMAZON WEB SERVICES AND MICROSOFT WINDOWS AZURE
- IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK DATA MINING ALGORITHM: A CASE STUDY OF BIRTH REGISTRATION DATA
Last modified: 2019-03-05 22:29:41