Face recognition using extended isomap

Research output: Contribution to conferencePaperpeer-review

58 Citations (Scopus)


The Isomap method has demonstrated promising results in finding low dimensional manifolds from samples in the high dimensional input space. While conventional subspace methods compute L1 or L2 metrics to represent distances between samples and apply Principal Component Analysis or alike to induce linear manifolds, the Isomap method estimates geodesic distance between samples and then uses multidimensional scaling to induce a low dimensional manifold. Since the Isomap method is developed based on reconstruction principle, it may not be optimal from the classification viewpoint. In this paper, we present an extended Isomap method that utilizes-Fisher Linear Discriminant for pattern classification. Numerous experiments on image data sets show that our extension is more effective than the original Isomap method for pattern classification. Furthermore, the extended Isomap method shows promising results compared with best classification methods in the literature.

Original languageEnglish
Publication statusPublished - 2002
EventInternational Conference on Image Processing (ICIP'02) - Rochester, NY, United States
Duration: 2002 Sept 222002 Sept 25


OtherInternational Conference on Image Processing (ICIP'02)
Country/TerritoryUnited States
CityRochester, NY

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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