The Common Vector (CV) method is a successful method which has been originally proposed for isolated word recognition problems in the case where the number of samples for each class is less than or equal to the dimensionality of the sample space. This method suggests elimination of all the features that are in the direction of the eigenvectors corresponding to the nonzero eigenvalues of the covariance matrix for each class. The feature vectors obtained after this operation are unique for each class and called common vectors. Recently, a similar method called the Discriminative Common Vector (DCV) method has been proposed for face recognition problems. Instead of using a given class' own covariance matrix, this method uses the within-class scatter matrix of all classes to obtain the common vectors. Then, PCA is applied to the common vectors to obtain the final projection vectors. In this paper we apply the CV method to the face recognition problem and compare the CV and the DCV methods in terms of recognition accuracy, training time efficiency, storage requirements, and real-time performance.