The Scale Invariant Feature Transform (SIFT) proposed by David G. Lowe has been used in face recognition and proved
to perform well. Recently, a new detector and descriptor, named Speed-Up Robust Features (SURF) suggested by
Herbert Bay, attracts people's attentions. SURF is a scale and in-plane rotation invariant detector and descriptor with
comparable or even better performance with SIFT. Because each of SURF feature has only 64 dimensions in general and
an indexing scheme is built by using the sign of the Laplacian, SURF is much faster than the 128-dimensional SIFT at
the matching step. Thus based on the above advantages of SURF, we propose to exploit SURF features in face
recognition in this paper.