Weak Classifiers Selecting based on PSO in AdaBoost

Weak classifiers selection plays an important role in face detection based on AdaBoost algorithm. More discriminative weak classifiers can not only reduce training time but also enhance classification accuracy. In this paper, a new weak classifiers selecting algorithm based on PSO is proposed to make improvement in weak classifier selection. First, we eliminate some less discriminative features in the feature formation stage to select weak classifiers, rather than select weak classifiers using all the features. Second, the weak classifiers are constructed by using PSO to select the best features and the best thresholds, and then combine these key weak classifiers into a more effective strong classifier. Experimental results indicate that the method can effectively solve the problem of too much training time and gain higher training efficiency.