基于改进的自适应局部保持投影算法的人脸识别  

Face Recognition Based on Improved Adaptive Locality Preserving Projection

作　　者：梅玲玲[1] 龚劬[1] MEI Ling-ling GONG Qu(College of Mathematics and Statisties,Chongqing University,Chongqing 401331 ,China)

机构地区：[1]重庆大学数学与统计学院,重庆401331

出　　处：《计算机科学》2016年第8期286-291,共6页Computer Science

基　　金：有限角逆向螺旋锥束CT扫描与图像重建(61271313)资助

摘　　要：局部保持投影(LPP)通过构造近邻图来保持样本的局部结构,在构造近邻图的过程中,LPP会遇到两个参数K和σ的选择问题。近邻图的构建对算法的识别效果起着重要的作用,因而这两个参数的选择会在很大程度上影响LPP的识别率。为了避免参数的选择对识别率造成影响,提出了一种基于改进的自适应局部保持投影的人脸识别算法。首先,构造无参数的近邻图,其能够自适应地选取样本的近邻点并确定其相应的边权。其次,由于在计算过程中出现了矩阵维数过高的问题,因此采用QR分解进行降维处理。最后,利用共轭正交化使得投影轴具有统计不相关性,以降低特征矢量间的统计相关性,提高识别率。在ORL人脸库和YALE人脸库上进行了实验,结果表明改进的算法在识别率方面整体上好于LPP算法、DLPP算法、LMMC算法。Locality preserving projections （LPP） aims to preserve local structure of the data by constructing a nearest- neighbor graph. In the construction process of nearest-neighbor graph, LPP will encounter the difficulty of the selection of two parameters K and a. The construction of nearest-neighbor graph plays an important role in recognition effect, so selection of the two parameters can affect the discrimination ability of LPP. In order to avoid the effects of the selection of parameters on recognition rate, an face recognition algorithm based on improved adaptive locality preserving projec- tion was proposed. Firstly, a parameter-free graph construction strategy is designed, which can adaptively choose neigh- bors of each sample point and determine corresponding edge weights. Then, because of the high dimensionality problem in the matrix calculation process, QR decomposition is used to reduce dimension. Finally, conjugate orthogonalization is used to reduce the statistical correlation between feature vectors and improve the recognition rate by ensuring that the projection axis has statistical uncorrelation. The experimental results on ORL database show that the new algorithm is better than the LPP algorithm,DLPP algorithm, and LMMC algorithm in terms of recognition rate.

关 键 词：近邻图 自适应局部保持投影 人脸识别 共轭正交 统计不相关 降维 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]