针对传统的局部保持投影算法(LPP)直接使用数据的原始空间信息导致选取近邻不准确,以及LPP算法投影时忽略数据类别信息的问题,提出一种基于自适应近邻局部保持投影的人脸识别方法。该方法在特征提取时利用可变的相似度、近邻信息以及数据类别信息构建目标函数,使得在投影子空间中同类样本尽量紧凑,异类样本尽量远离。通过最小化目标函数自适应优化邻接矩阵与投影矩阵,用优化后的投影矩阵对高维数据进行降维,采用降维后的数据进行人脸分类识别。将该方法应用于扩展Yale人脸数据库、CMU-PIE人脸数据库、MSRA人脸数据库和CAS-PEAL人脸数据库中进行人脸识别,实验结果验证了其有效性。

Traditional locality preserving projection (LPP) algorithm directly uses the spatial information of original data, which leads to inaccurately select neighborhood, and ignores data categories information of projection of LPP algorithm. To solve these problems, a face recognition method is proposed based on adaptive neighborhood LPP. In the feature extraction, the objective function is constructed based on the variable similarity, neighborhood information, and data categories information, so that the same class samples are close and different class samples are far away from each other in projected subspace. Adjacency matrix and projection matrix are adaptably optimized by minimizing objective function. Optimized projection matrix is used to reduce the dimension of high-dimensional face data, and low-dimensional data is used to classify and recognize face samples. The experimental results on Yale B, PIE, MSRA and CAS-PEAL databases validate the effectiveness of the proposed algorithm.