Face recognition based on multi-AdaBoost

Yi Zhang; Weihong Cui

doi:10.1117/12.831986

30 October 2009 Face recognition based on multi-AdaBoost

Yi Zhang, Weihong Cui

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74960G (2009) https://doi.org/10.1117/12.831986
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Yi Zhang and Weihong Cui "Face recognition based on multi-AdaBoost", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960G (30 October 2009); https://doi.org/10.1117/12.831986

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