Face recognition is an important technique which can be used in many applications. In recent years, face recognition has
attracted large amount of research interest. Many recognition methods have been proposed, however, most of them are
not able to make use of local salient features to effectively capture the face information. Recently, SIFT has been
proposed for object matching in image retrieval area, and it proves to be a powerful matching tool. In this paper, we
applied and studied SIFT method on face recognition, and compared it with the well known face recognition methods in
literature, i.e., PCA and 2DPCA. Rigorous tests were carried out on 3 major face databases. Our results show SIFT has
significant advantages over both PCA and 2DPCA in terms of recognition rate and number of training samples. This
paper also points out some shortcomings of classic experiment method to recognize faces and improve them.