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Now a days research is going on to design a high performance automatic face recognition system which is really a challenging task for researchers. As faces are complex visual stimuli that differ dramatically, hence developing an efficient computational approach for accurate face recognition is very difficult. In this paper a high performance face recognition algorithm is developed and tested using conventional Principal Component Analysis (PCA) and two dimensional Principal Component Analysis (2DPCA). These statistical transforms are exploited for feature extraction and data reduction. We have proposed here to assign different weight to the only very few nonzero eigenvalues related eigenvectors which are considered as non-trivial principal components for classification. Lastly face recognition task is performed by k-nearest distance measurement. Experimental results on ORL and YALE face databases show that the proposed method improves the performance of face recognition with respect to existing techniques. The results show that better recognition performance can be achieved with less computational cost than that of other existing methods.

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