A new nonlinear feature extraction method for face recognition
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.
feature extraction face recognition linear discriminant analysis subspace learning manifold learning