Abstract

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.

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