随机采样的2DPCA人脸识别方法  

Two-dimensional PCA Based on Random Sampling for Face Recogniton

作　　者：朱玉莲[1] 彭星[1] 

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京210016

出　　处：《小型微型计算机系统》2011年第12期2461-2465,共5页Journal of Chinese Computer Systems

基　　金：南京航空航天大学基本科研业务费专项科研项目(NS2010233)资助

摘　　要：在2DPCA的基础上提出一种随机采样的2DPCA人脸识别方法--RRS-2DPCA.同传统通过对特征或投影向量进行采样的方法不同的是,RRS-2DPCA(Row Random Sampling 2DPCA)将随机采样建立于图像的行向量集中,然后在行向量子集中执行2DPCA.在ORL、Yale和AR人脸数据集上进行实验,结果表明RRS-2DPCA不仅具很好的识别性能和运算效率,而且对参数具有很大的稳定性.另外针对2DPCA和RRS-2DPCA对光线、遮挡等不鲁棒问题,进一步提出了局部区域随机采样的2DPCA方法LRRS-2DPCA(Local Row Random Sampling 2DPCA),将RRS-2DPCA执行在人脸图像的局部区域中.实验结果表明LRRS-2DPCA不仅具有较好的鲁棒性更大大的提高了RRS-2DPCA的识别性能.Proposed a 2DPCA method based on random sampling,termed as Row Random Sampling 2DPCA（RRS-2DPCA）,for face recognition.Different from those traditional face recognition methods which sampling from feature or feature vector,RRS-2DPCA constructs random sampling on row vector sets and then performs 2DPCA on those row vector sets.The experimental results on ORL,Yale and AR face databases show that RRS-2DPCA not only obtains very good recogntion accuracy and computational efficiency,but also is stable to the number of random sampling row corresponding to different database.In additional,in order to relax the nonrobust of 2DPCA and RRS-2DPCA to occlusion,we further proposed local region random sampling 2DPCA（LRRS-2DPCA）,which performs RRS-2DPCA on local regions of face image.The experimental results indicates that LRRS-2DPCA gains better both relative robustness and good recognition accuracy than RRS-2DPCA.

关 键 词：人脸识别 二维主成分分析(2DPCA) 局部区域 随机采样 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]