Dimensionality reduction based on nonparametric discriminant analysis with kernels for feature extraction and recognition
Junbao Li, Shu‐Chuan Chu, Jeng‐Shyang Pan · 2010
Dimensionality reduction is the most popular method for feature extraction and recognition. Recently, Li et al. (IEEE PAMI, 2009) proposed Nonparametric Discriminant Analysis (NDA) based dimensionality reduction for face recognition and reported an excellent recognition performance. However, NDA has its limitations on extracting the nonlinear features of face images for recognition, and owing to the highly nonlinear and complex distribution of face images under a perceivable variation in viewpoint, illumination or facial expression. In order to increase the NDA, we extend the NDA with kernel trick to propose Nonparametric Kernel Discriminant Analysis (NKDA) for feature extraction and recognition. Experimental results on ORL, YALE and UMIST face databases show that NKDA outperforms NDA on recognition, which demonstrates that it is feasible to improve NDA with kernel trick for feature extraction.