Face recognition based on multi-AdaBoost
Yi Zhang, Weihong Cui · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
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.