Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8659
Title: Human face recognition based on spatially weighted Hausdorff distance
Authors: Guo, B
Lam, KM 
Lin, KH
Siu, WC 
Keywords: Face recognition
Facial feature detection
Modified Hausdorff distance
Principal component analysis
Issue Date: 2003
Publisher: North-Holland
Source: Pattern recognition letters, 2003, v. 24, no. 1-3, p. 499-507 How to cite?
Journal: Pattern recognition letters 
Abstract: The edge map of a facial image contains abundant information about its shape and structure, which is useful for face recognition. To compare edge images, Hausdorff distance is an efficient measure that can determine the degree of their resemblance, and does not require a knowledge of correspondence among those points in the two edge maps. In this paper, a new modified Hausdorff distance measure is proposed, which has a better discriminant power. As different facial regions have different degrees of significance for face recognition, a new modified Hausdorff distance is proposed which is weighted according to a weighted function derived from the spatial information of the human face; hence crucial regions are emphasized for face identification. Experimental results show that the distance measure can achieve recognition rates of 80%, 87%, and 91% for the first, the first five, and the first seven likely matched faces, respectively.
URI: http://hdl.handle.net/10397/8659
ISSN: 0167-8655
EISSN: 1872-7344
DOI: 10.1016/S0167-8655(02)00272-6
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