基于子模式行列方向二维线性判别分析特征融合的特征提取  

Feature extraction using a fusion method based on sub-pattern row-column two-dimensional linear discriminant analysis

作　　者：董晓庆[1] 陈洪财[1] 

机构地区：[1]韩山师范学院物理与电子工程系,广东潮州521041

出　　处：《计算机应用》2014年第12期3593-3598,共6页journal of Computer Applications

基　　金：国际科技合作项目(2011DFR90720);2013年教育部高等学校"专业综合改革试点"项目(ZG0411);2013年韩山师范学院青年基金资助项目(LQ201302);2014年广东省高等教育教学改革项目(GDJG20142402)

摘　　要：针对人脸识别中表情和光照变化引起的面部变化、灰度不均匀等识别问题,提出一种基于子模式行列方向二维线性判别分析(Sp-RC2DLDA)的特征提取方法。该方法通过对原图像进行子模式分块处理,能有效提取图像的局部特征,减少表情、光照变化的影响,通过把相同位置的子图像组成子样本集,合理利用了子块间的空间关系,进一步提高了识别率;同时,对各个子样本集分别利用行方向二维线性判别分析(2DLDA)和列方向扩展2DLDA(E2DLDA)进行特征抽取,得到互补的行、列方向子图像特征,并分别把子图像特征组合成原图像的特征矩阵,然后利用一种特征融合方法对行、列方向特征矩阵进行有效融合,对互补的特征空间进行融合有效地改善了识别性能;最后采用最近邻分类器进行人脸识别实验。在Yale及ORL人脸库上的实验结果表明,Sp-RC2DLDA有效地减少了表情和光照变化的影响,具有较好的鲁棒性。In order to solve the problems, such as facial change and uneven gray, caused by the variations of expression and illumination in face recognition, a novel feature extraction method based on Sub-pattern Row-Column Two-Dimensional Linear Discriminant Analysis （Sp-RC2DLDA） was proposed. In the proposed method, by dividing the original images into smaller sub-images, the local features could be extracted effectively, and the impact of variations in facial expression and illumination was reduced. Also, by combining the sub-images at the same position as a subset, the recognition performance could be improved for making full use of the spatial relationship among sub-images. At the same time, two classes of features which complemented each other can be obtained by synthesizing the local sub-features which were achieved by performing 2DLDA （Two-Dimensional Linear Discriminant Analysis） and Extend 2DLDA （E2DLDA） on a set of partitioned sub-patterns in the row and column directions, respectively. Then, the recognition performance was expected to be improved by employing a fusion method to effectively fuse these two classes of complementary features. Finally, nearest neighbor classifier was applied for classification. The experimental results on Yale and ORL face databases show that the proposed Sp-RC2DLDA method reduces the influence of variations in illumination and facial expression effectively, and has better robustness and classification performance than the other related methods.

关 键 词：人脸识别 特征抽取 扩展二维线性判别分析 子模式 特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]