Face recognition with MRC-boosting

Xun Xu, Thomas S. Huang

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


In this paper, a novel classification algorithm called MRC-Boosting is proposed. Through aggregating Maximal-Rejection-Classifier features under boosting framework, this algorithm can deal with complicated two-class classification problem, especially for the category called target detection problem where a target class should be discriminated from the surrounding clutter class. MRC-Boosting is efficient since unlike many other boosting based algorithms, at each iteration the optimal feature is computed in closed-form, with neither exhaustive search nor time-consuming numerical optimization. Furthermore, a variant of MRC-Boosting is derived and applied to face recognition. This variant MRC-Boosting algorithm is able to utilize large amount of training samples efficiently, overcoming the difficulty faced by other algorithms like AdaBoost. The effectiveness of the proposed algorithm is validated by face recognition experiments on CMU-PIE database.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Number of pages8
StatePublished - 2005
EventProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 - Beijing, China
Duration: Oct 17 2005Oct 20 2005

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


OtherProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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