A New Approach using Modified Hausdorff Distances with EigenFace for Human Face Recognition

Kwan Ho Lin, Kin Man Lam, Wan Chi Siu

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

5 Citations (Scopus)

Abstract

Hausdorff distance is an efficient measure of the similarity of two point sets. In this paper, we propose two new spatially weighted Hausdorff distance measures for human face recognition, namely, spatially eigen-weighted Hausdorff distance (SEWHD) and spatially eigen-weighted 'doubly' Hausdorff distance (SEW2HD). These new Hausdorff distances incorporate the information about the location of important facial features so that distances at those regions will be emphasized. The weighting function used in the Hausdorff distance measure is based on an eigenface, which has a large value at locations of important facial features and can reflect the face structure more effectively. 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
Title of host publicationProceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARCV 2002
Pages980-984
Number of pages5
Publication statusPublished - 1 Dec 2002
EventProceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARC 2002 - Singapore, Singapore
Duration: 2 Dec 20025 Dec 2002

Conference

ConferenceProceedings of the 7th International Conference on Control, Automation, Robotics and Vision, ICARC 2002
CountrySingapore
CitySingapore
Period2/12/025/12/02

ASJC Scopus subject areas

  • Engineering(all)

Cite this