Spatially eigen-weighted Hausdorff distances for human face recognition

Kwan Ho Lin, Kin Man Lam, Wan Chi Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

55 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1827-1834
Number of pages8
JournalPattern Recognition
Volume36
Issue number8
DOIs
Publication statusPublished - 1 Jan 2003

Keywords

  • Eigenface technique
  • Face recognition
  • Hausdorff distance

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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