Letters: A new nonlinear feature extraction method for face recognition

Authors:
Yanwei Pang;Zhengkai Liu;Nenghai Yu
Affiliations:
MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, P ...;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, P ...;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, P ...
Venue:
Neurocomputing
Year:
2006

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Abstract

Feature extraction is a crucial step for pattern recognition. In this paper, a nonlinear feature extraction method is proposed. The objective function of the proposed method is formed by combining the ideas of locally linear embedding (LLE) and linear discriminant analysis (LDA). Optimizing the objective function in a kernel feature space, nonlinear features can be extracted. A major advantage of the proposed method is that it makes full use of both the nonlinear structure and class-specific information of the training data. Experimental results on the AR face database demonstrate the effectiveness of the proposed method.