Boosting Probabilistic Reasoning Model for Face Recognition
Yuntao Qian · Jisuanji gongcheng · 2005
In a wide range of applications,the combination of classifiers leads to substantial reduction of misclassification error.This paper proposes a new algorithm to boost performance of probabilistic reasoning model(PRM) face recognition methods.In this algorithm,it divides the classification task into some sub-classifiers,each sub-classifier concentrates on some labels that are hardest to discriminate.Finally,it combines all these sub-classifiers to form a very strong class ifier experimental result indicates that the proposed methodology enhances performance of the probabilistic reasoning model with an average improvement of correct recognition rate(CRR) up to 1.8%.