关键词: 降维, 流形学习, 人脸识别, 邻域保持嵌入, 正交, 不相关

Abstract: Neighborhood Preserving Embedding (NPE) is still an unsupervised method in nature, and it does not take advantage of the existing classification information to improve the classification efficiency. Therefore, two supervised manifold learning methods named Orthogonal Marginal Neighborhood Preserving Embedding (OMNPE) and Uncorrelated Marginal Neighborhood Preserving Embedding (UMNPE) were proposed. Both methods firstly constructed within-class graph and between-class graph and defined within-class reconstructive error and between-class reconstructive error. Then, OMNPE and UMNPE sought to find a projection that simultaneously minimized the within-class reconstructive error and maximized the between-class reconstructive error, under the orthogonal and uncorrelated constraint conditions, respectively. The training samples and testing samples were projected onto low-dimensional subspace respectively. Finally, the nearest neighbor classifier was used for classification. Extensive experiments in ORL and Yale face databases illustrate that the proposed algorithms outperform those of subspace face recognition algorithms with average recognition rate by 0.5%~3%, such as Linear Discriminant Analysis (LDA), Marginal Fisher Analysis (MFA), which proves the effectiveness of the proposed algorithms.

Key words: dimensionality reduction, manifold learning, face recognition, Neighborhood Preserving Embedding (NPE), orthogonal, uncorrelated