【摘要】 利用LLE非线性降维方法提取人脸特征,然后将提取出来的特征输入到BP神经网络进行训练得到人脸类间的判别信息,进行人脸识别。利用LLE降维方法既能够降低数据维数,减少运算量,又很好的保留了各类人脸样本的拓扑结构,避免人脸图像光照、姿态等因素对人脸识别的影响。在ORL人脸库上的实验结果表明了,这种方法是有效的。更多还原

【Abstract】 The locally linear embedding(LLE) algorithm is presented to extract the face feature in the paper.And then the feature coefficients of each sample are trained in the BP neural networks for face recognition.The locally linear embedding algorithm can reveal the intrinsic distribution of data,which cannot be provided by classical linear dimensionality reduction methods.The nonlinear structure in high dimensional data space was exploited with the local symmetries of linear reconstructions.The data points in high dimensional space were mapped into corresponding data points in lower dimensional space under preserving distance between data points.In this paper,the LLE algorithm reduces the dimensionality of image data and reveals the intrinsic distribution of data.And this method also has invariability in translation and rotation.Experiment results on ORL database demonstrate that the method is effective.更多还原