Optimal feature extraction methods for classification methods and their applications to biometric recognition

Classification is often performed after feature extraction. To improve the recognition performance, we could develop the optimal feature extraction method for a classification method. In this paper, we propose three feature extraction methods Discriminative Projection for Nearest Neighbor (DP-NN), Discriminative Projection for Nearest Mean (DP-NM) and Discriminative Projection for Nearest Feature Line (DP-NFL), which are optimal for classification methods Nearest Neighbor (NN), Nearest Mean (NM) and Nearest Feature Line (NFL), respectively. We also prove that DP-NN and DP-NM are equivalent to Linear Discriminant Analysis (LDA) under a certain condition. In the experiments, LDA, DP-NFL and SRC steered Discriminative Projection (SRC-DP) are used for feature extraction and then the extracted features are classified by NN, NM, NFL, Sparse Representation based Classification (SRC) and Collaborative Representation Classifier (CRC). Experimental results of biometric recognition show that the proposed DP-NFL performs well, and that combining an effective classification method with the optimal feature extraction method for it can perform best.