基于局部保留映射与径向基网络的人脸识别方法

局部保留映射（locality preserving projections，LPP）选择人脸子空间特征包含非线性信息而不利于最近邻法分类．基于径向基函数（radial basis function，RBF）分类器可以将非线性可分问题转化为线性可分问题的特点，提出了利用LPP子空间和RBF网络相结合进行人脸识别的方法，LPP算法采用监督模式，RBF网络隐层中心采用正交最小二乘（orthogonal least—squares，OLS）法训练．实验结果表明，该方法在Yale—B和Yale—B Extended人脸数据库上的识别率为95．67％，在CMU—PIE人脸数据库上的识别率为98．52％，具有较好的抗噪能力，识别效果优于特征脸、Fisher脸以及拉普拉斯脸法．

Face Recognition Based on Locality Preserving Projections and Radial Basis Function Network

Face subspace features selected by locality preserving projections （ LPP ） contain non-linear information which leads to the failure of using the nearest neighbor classifier to recognition. Therefore, a face recognition method combining LPP subspace with radial basis function （ RBF ） classifier was proposed according to the advantage of RBF that could convert non-linear separable problem to a linear separability. In this paper, LPP was used in supervised mode and the hidden center of RBF network was trained with orthogonal least-squares （OLS） method.Extensive experimental results show that the recognition rates of the proposed method on Yale-B, Yale-B Extended face databases and CMU-PIE face database are 95.67% and 98.52%, respectively.The improved method has better anti-noise capability and its recognition rate is better than those of Eigenfaces, Fisherfaces and Laplacianfaces.