Efficient improvement for adaboost based face detection system

The training of the adaboost algorithm for face detection is time costly; it often needs days or weeks in the previous system. In this paper, we describe efficient optimization techniques and implement skills to reduce the training time. First we use some preprocessing technique to reduce the candidate features size to ten percent of the original, and then we use some implement skills to further reduce the training time. Besides these, we use double thresholds to describe each feature, which can improve the efficient of each feature, and reduce the required feature number for the final strong classifier. The experiment result show that the training of our system is hundred time faster than previous systems.