Document Type
Thesis
Date of Award
Summer 8-31-2003
Degree Name
Master of Science in Computer Science - (M.S.)
Department
Computer Science
First Advisor
Chengjun Liu
Second Advisor
Joseph Y-T. Leung
Third Advisor
Marvin K. Nakayama
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.
Recommended Citation
Larkin, Timothy Kevin, "Face recognition using principal component analysis" (2003). Theses. 652.
https://digitalcommons.njit.edu/theses/652
