Adaboost face detection algorithm based on correlation of classifiers

In order to enhance the ensemble of the traditional Adaboost algorithm and reduce its complexity, two improved Adaboost algorithms are proposed, which are based on the correlation of classifiers. In the algorithm, Q-statistic is added in the training weak classifiers, every weak classifier is related not only to the current classifier, but also to previous classifiers as well, which can effectively reduce the weak classifier similarity. Simulations result in CMU (Carnegies Mellon University) show that the algorithms are of better detection rate and lower false alarm rate, compared with traditional Adaboost algorithm and FloatBoost.