Share Email Print
cover

Proceedings Paper

Face recognition using SURF features
Author(s): Geng Du; Fei Su; Anni Cai
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749628 (30 October 2009); doi: 10.1117/12.832636
Show Author Affiliations
Geng Du, Beijing Univ. of Posts and Telecommunications (China)
Fei Su, Beijing Univ. of Posts and Telecommunications (China)
Anni Cai, Beijing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

© SPIE. Terms of Use
Back to Top