Date of Award

Summer 2003

Document Type

Thesis

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.

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