摘要：主成分分析（PCA）的人脸识别算法，以减少的特征向量是涉及到对抽象的特点，改进了主成分分析（一）iUumination算法的变化影响酶原sed.The方法是基于上减低与正常化其相应的标准差的特征向量元素相关联的大特征值的特征向量的影响力的想法。耶鲁大学和耶鲁大学面临的数据库面对数据库B是用来验证-Abstract：In principal component analysis(PCA)algorithms for face recognition，to reduce the influence of the

eigenvectors which relate to the changes of the iUumination on abstract features，a modified PCA ( A)

algorithm is propo sed．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．

Th e Yale face database and Yale face database B are used to verify the method．The simulation results show

that，f0r front face and even under the condition of limited variation in the facial po ses the proposed method

results in better perform ance than the conventional PCA and linear discriminant analysis(LDA)approaches．and

the computational cost remains the same as that ofthe PCA，and much less than that ofthe LDA．