Domain-Partitioning Rankboost for Face Recognition

In this paper we propose a domain partitioning RankBoost approach for face recognition. This method uses Local Gabor Binary Pattern Histogram (LGBPH) features for face representation, and adopts RankBoost to select the most discriminative features. Unlike the original RankBoost algorithm in Freund et al. (2003), weak hypotheses in our method make their predictions based on a partitioning of the similarity domain. Since the domain partitioning approach handles the loss function of a ranking problem directly, it can achieve a higher convergence speed than the original approach. Furthermore, in order to improve the algorithm´s generalization ability, we introduce some constraints to the weak classifiers being searched. Experiment results on FERET database show the effectiveness of our approach.