Human face recognition using a spatially weighted Hausdorff distance

Baofeng Guo, Kin Man Lam, Wan Chi Siu, Shuyuan Yang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

10 Citations (Scopus)


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, Hausdroff 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 noise immunity capability and better discriminant power. As the different facial regions have different relative importance for face recognition, the modified Hausdorff distance 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 82%, 93%, and 97% for the first, the first three, and the first five likely matched faces, respectively.
Original languageEnglish
Title of host publicationISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings
Number of pages4
Publication statusPublished - 1 Dec 2001
Event2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001 - Sydney, NSW, Australia
Duration: 6 May 20019 May 2001


Conference2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001
CitySydney, NSW

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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