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Principal components analysis (PCA) is a basic method widely used in face feature extraction and recognition. In order to overcome the shortcoming of absent consideration of the between-class information and the defect of the inconvenient update of the eigen-space in the traditional PCA method, this paper proposed a cluster-based feature projection method. The method enlarges the difference of samples in the different classes, while the difference of the same classes is reduced. Experimental results on ORL face database show that the method has higher correct recognition rate and higher recognition speeds than traditional PCA algorithm.