Abstract: Neighborhood preserving embedding (NPE) algorithm based on manifold learning theory can discover the intrinsic structure behind data set.But in the scenery of face recognition,algorithm can’t detect the intrinsic structure accurately due to the insufficient of data,sequentially,the performance of NPE is influenced.In order to solve the problems of NPE in face recognition,a supervised collaborative neighborhood preserving projection (SCNPP) algorithm was presented.The proposed algorithm constructs the neighborhood graph under the guidance of category information,makes the geometric relationship between the same samples be preserved effectually,utilizes the collaborative representation to remedy the representation errors of NPE caused by the lack of data,calculates the projection matrix with a weight matrix which preserves the neighborhood relationship effectually and discoveries the intrinsic manifold structure of data accurately,improves the performance of classification.Extensive experiments on popular face databases (FERET,AR and Extended Yale B) verify the effectiveness of the proposed method.

Key words: Face recognition,Manifold learning,Neighborhood preserving embedding,Collaborative representation,Supervised collaborative neighborhood preserving projection