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.