|Table of Contents|

[1] Luo Lin, Zou Cairong, Yang Fengfan,. Modified algorithm of principal component analysisfor face recognition [J]. Journal of Southeast University (English Edition), 2006, 22 (1): 26-30. [doi:10.3969/j.issn.1003-7985.2006.01.006]
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Modified algorithm of principal component analysisfor face recognition()
改进的人脸识别主分量分析算法
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
22
Issue:
2006 1
Page:
26-30
Research Field:
Electronic Science and Engineering
Publishing date:
2006-03-20

Info

Title:
Modified algorithm of principal component analysisfor face recognition
改进的人脸识别主分量分析算法
Author(s):
Luo Lin1;  Zou Cairong1;  Yang Fengfan2
1Department of Radio Engineering, Southeast University, Nanjing 210096, China
2Department of Electronics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
罗琳1;  邹采荣1;  仰枫帆2
1东南大学无线电工程系, 南京 210096; 2南京航空航天大学电子工程系, 南京 210016
Keywords:
face recognition;  principal component analysis;  linear discriminant analysis
人脸识别;  主分量分析;  线性判别分析
PACS:
TN391
DOI:
10.3969/j.issn.1003-7985.2006.01.006
Abstract:
In principal component analysis(PCA)algorithms for face recognition, to reduce the influence of the eigenvectors which relate to the changes of the illumination on abstract features, a modified PCA(MPCA)algorithm is proposed.The method is based on the idea of reducing the influence of the eigenvectors associated with the large eigenvalues by normalizing the feature vector element by its corresponding standard deviation.The Yale face database and Yale face database B are used to verify the method.The simulation results show that, for front face and even under the condition of limited variation in the facial poses, the proposed method results in better performance than the conventional PCA and linear discriminant analysis(LDA)approaches, and the computational cost remains the same as that of the PCA, and much less than that of the LDA.
在应用于人脸识别领域的主分量分析(PCA)算法中, 为了降低与外界光照变化相关的特征向量对提取特征的影响, 提出了一种改进的主分量分析(MPCA)算法, 利用相对应的标准方差对提取的特征矢量元素进行归一化处理.采用耶鲁大学的2个人脸数据库(Yale face database 和Yale face database B)进行了验证, 实验结果表明, 对于正面人脸和具有小角度姿态变化情况下的人脸, 提出方法的性能优于传统的PCA和LDA(线性判别分析)算法, 而运算量和PCA算法相同, 大大低于LDA算法.

References:

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Memo

Memo:
Biography: Luo Lin(1969—), female, doctor, associate professor, luolin@seu.edu.cn.
Last Update: 2006-03-20