New supervised locality-preserving projections algorithm for face recognition
一种新的有监督保局投影人脸识别算法

In order to make full use of the classification information of samples to get optimal features, a new Supervised Locality Preserving Projections (NSLPP) algorithm for face recognition was proposed. Between-class scatter matrix was embedded in the objective function of original locality preserving projections, and the transformation matrix could be obtained based on the modified objective function. Subsequently, according to the idea of linear discriminant, the optimal base vectors of the transformation matrix were selected to form the final transformation matrix. As a result, the features of training samples and testing samples were got by projecting them on the subspace spanned by optimal base vectors. Finally, Nearest Neighborhood (NN) algorithm was used to construct classifiers. Experiments on ORL and FERET face database show that the recognition performance of NSLPP is effective.