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