The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Face recognition using principal component analysis
Author:	Larkin, Timothy Kevin
View Online:	njit-etd2003-093 (xii, 60 pages ~ 7.2 MB pdf)
Department:	Department of Computer Science
Degree:	Master of Science
Program:	Computer Science
Document Type:	Thesis
Advisory Committee:	Liu, Chengjun (Committee chair) Leung, Joseph Y-T. (Committee member) Nakayama, Marvin K. (Committee member)
Date:	2003-08
Keywords:	Face recognition Kernal Principal Component Analysis (KCPA)
Availability:	Unrestricted
Abstract:	Current methods of face recognition use linear methods to extract features. This causes potentially valuable nonlinear features to be lost. Using a kernel to extract nonlinear features should lead to better feature extraction and, therefore, lower error rates. Kernel Principal Component Analysis (KPCA) will be used as the method for nonlinear feature extraction. KPCA will be compared with well known linear methods such as correlation, Eigenfaces, and Fisherfaces.