东南大学学报（英文版）2006，Vol.22Issue(1)：26-30,5.
改进的人脸识别主分量分析算法
Modified algorithm of principal component analysis for face recognition
罗琳 1邹采荣 1仰枫帆2
作者信息
- 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
- 折叠
摘要
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.关键词
人脸识别/主分量分析/线性判别分析Key words
face recognition/principal component analysis/linear discriminant analysis分类
信息技术与安全科学引用本文复制引用
罗琳,邹采荣,仰枫帆..改进的人脸识别主分量分析算法[J].东南大学学报（英文版）,2006,22(1):26-30,5.
