Human face recognition based on spatially weighted Hausdorff distance

Baofeng Guo, Kin Man Lam, Kwan Ho Lin, Wan Chi Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

40 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)499-507
Number of pages9
JournalPattern Recognition Letters
Volume24
Issue number1-3
DOIs
Publication statusPublished - 1 Jan 2003

Keywords

  • Face recognition
  • Facial feature detection
  • Modified Hausdorff distance
  • Principal component analysis

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

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