Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26755
Title: Spatially eigen-weighted Hausdorff distances for human face recognition
Authors: Lin, KH
Lam, KM 
Siu, WC 
Keywords: Eigenface technique
Face recognition
Hausdorff distance
Issue Date: 2003
Publisher: Elsevier
Source: Pattern recognition, 2003, v. 36, no. 8, p. 1827-1834 How to cite?
Journal: Pattern recognition 
Abstract: Hausdorff distance is an efficient measure of the similarity of two point sets. In this paper, we propose a new spatially weighted Hausdorff distance measure for human face recognition. The weighting function used in the Hausdorff distance measure is based on an eigenface, which has a large value at locations of importance facial features and can reflect the face structure more effectively. Two modified Hausdorff distances, namely, "spatially eigen-weighted Hausdorff distance" (SEWHD) and "spatially eigen-weighted 'doubly' Hausdorff distance" (SEW2HD) are proposed, which incorporate the information about the location of important facial features such as eyes, mouth, and face contour so that distances at those regions will be emphasized. Experimental results based on a combination of the ORL, MIT, and Yale face databases show that SEW2HD can achieve recognition rates of 83%, 90% and 92% for the first one, the first three and the first five likely matched faces, respectively, while the corresponding recognition rates of SEWHD are 80%, 83% and 88%, respectively.
URI: http://hdl.handle.net/10397/26755
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/S0031-3203(03)00011-6
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