| 改进的模块2DPCA人脸识别方法 |
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| 引用本文： | 张龙翔.改进的模块2DPCA人脸识别方法[J].计算机工程与应用,2010,46(13):147-150. |
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| 作者姓名： | 张龙翔 |
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| 作者单位： | 临沂师范学院 信息学院，山东 临沂 276005 |
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| 摘 要： | 提出了一种基于类内自适应加权平均值的模块2DPCA人脸识别方法。该算法对每一类训练样本中每个训练样本的每一子块求类内自适应加权平均值，并用类内自适应加权平均值对训练样本类内的相应子块进行规范化处理，然后由所有规范化后的子块构成总体散布矩阵，从而得到最优投影矩阵；由训练集的全体子块的加权平均值对训练样本的子块和测试样本的子块进行规范化后投影到最优投影矩阵，得到识别特征；最后用最近距离分类器分类。在ORL人脸库上的实验结果表明，提出的方法在识别性能上明显优于2DPCA方法和普通模块2DPCA方法。
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| 关 键 词： | 人脸识别 二维主成分分析 类内自适应加权平均值 |
| 收稿时间： | 2009-8-11 |
| 修稿时间： | 2009-11-4
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Face recognition method using improved modular 2DPCA |
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| ZHANG Long-xiang.Face recognition method using improved modular 2DPCA[J].Computer Engineering and Applications,2010,46(13):147-150. |
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| Authors: | ZHANG Long-xiang |
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| Affiliation: | Information College，Linyi Normal University，Linyi，Shandong 276005，China |
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| Abstract: | Modular 2DPCA method based on within-class weight average face recognition is presented.Firstly,the within-class weight average of each sub-image of all training samples in each class is calculated,and it is used to normalize corresponding sub-images of the within-class samples.After that,the optimal projecting matrix from general matrix that is made up of all normalized sub-images can be obtained accordingly.Secondly,when all sub-images of training samples and testing samples are projected to the optimal p... |
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| Keywords: | face recognition two-dimensional principal component analysis within-class weight average |
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