【摘要】 应用RBF神经网络作为分类器用于人脸识别。提出了两个重要的准则来估计RBF单元的初始宽度,这个宽度可以控制RBF神经网络分类器的泛化能力。PCA方法把训练样本集投影到特征脸空间,以减少维数。在PCA变换的基础上,作者进一步运用FLD方法,为分类找到一个最佳的子空间,使类间距离和类内距离之比最大化。在ORL数据库上进行了仿真,仿真结果表明,该算法具有高效性和有效性。更多还原

【Abstract】 The RBF neural network for classification is applied in face recognition. With two important criterion for estimating the initial width of RBF unit,the width can control the generalization ability of RBF neural network classifier. PCA method to the training sample set the projection to the face space,to reduce dimension. On the basis of the PCA transform,an optimal subspace classification makes the distance between the classes to maximize the ratio of the distance using FLD method. Simulation is conducted on the ORL database,and its results show that the algorithm is efficiency and effectiveness.更多还原