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oaioai:digitalcommons.njit.edu:theses-1651

Face recognition using principal component analysis

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Publication date
31 August 2003
Publisher
Digital Commons @ NJIT

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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Digital Commons @ New Jersey Institute of Technology (NJIT)

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Last time updated on 29/10/2019

This paper was published in Digital Commons @ New Jersey Institute of Technology (NJIT).

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