Determining discriminative anatomical point pairings using adaboost for 3D face recognition

Steven Cadavid, Jindan Zhou, Mohamed Abdel-Mottaleb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, we present a novel method for 3D face recognition using adaboosted geodesic distance features. Firstly, a generic model is finely conformed to each face model contained within a 3D face dataset. Secondly, the geodesic distance between anatomical point pairs are computed across each conformed generic model. Adaboost then generates a strong-classifier based on a collection of geodesic distances that are most discriminative for face recognition. Experiments conducted on the Face Recognition Grand Challenge (FRGC) database D collection indicate that the system can achieve over a 95% rank-one recognition rate.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages49-52
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - Jan 1 2009
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: Nov 7 2009Nov 10 2009

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2009 IEEE International Conference on Image Processing, ICIP 2009
CountryEgypt
CityCairo
Period11/7/0911/10/09

Keywords

  • 3D face recognition
  • 3D registration
  • Adaboost
  • Biometrics
  • Geodesic distance

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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