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．

ImprovedPCAFaceRecognitionAlgorithm.PDF

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