A new nonlinear feature extraction method for face recognition

作者：Pang YW^*; Liu ZK; Yu NH

来源：Neurocomputing, 2006, 69(7-9): 949-953.

DOI：10.1016/j.neucom.2005.07.005

• face recognition
• subspace learning
• linear discriminant analysis
• manifold learning
• feature extraction

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.

• 出版日期2006-3
• 单位Microsoft; 中国科学技术大学