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Orthogonal neighborhood preserving projections(ONPP)is a typical graph-based dimensionality reduction technique,which preserves not only the locality but also the local and global geometry of the height dimensional data, and has been successfuly applied tO face recognition.The supervised ONPP tries to find the optimal embedding of lo、Jl卜 dimensional subspace by setting up homogeneous adjacency graphic and minimizing the homogeneous local reconstruc— tion errors.However,it only uses the homogeneous information,which leads to unconspicuous structure of heterogeneous data.Motivated by this fact,we proposed a novel method called double adjacency graphs based orthogonal neigh— borhood preserving projections(DAG-ONPP).By introducing homogeneous and heterogeneous neighbor adjacency graphs,the homogeneous reconstructing errors will be as small as possible and the heterogeneous reconstructing errors will be more obvious after data being embedded in low-dimensional subspace.The results of the experiments on the ORL,Yale,YaleB and PIE databases demonstrate that the proposed method can markedly improve the classification a— bility of the original method and outperforms the other typical methods.

基于双邻接图正交近邻保持投影的人脸识别算法 (Double Adjacency Graphs Based Orthogonal Neighborhood Preserving Projections for Face Recognition).