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Proceedings Paper

Face recognition based on multi-AdaBoost
Author(s): Yi Zhang; Weihong Cui
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Paper Abstract

In going from two-class to multi-class classification, most boosting algorithms have been restricted to reducing multiclass problem to multiple two-class problems. In the paper, a direct multi-class AdaBoost algorithm is adopted to face recognition. Then the weighted classification trees are extended from stumps as weak learners to fulfill the multi-class learning. The multi-class boosting algorithm has the following features: A K-class classification problem is treated simultaneously without reducing it to multiple binary classification problems; only one lost function per iteration is fitted; the algorithmic structure is compact and easy to implement. The experimental results both on UCI dataset and YaleA face dataset show the meanings of the proposed algorithm.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960G (30 October 2009); doi: 10.1117/12.831986
Show Author Affiliations
Yi Zhang, Wuhan Univ. (China)
Weihong Cui, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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