动态权值预划分实值Adaboost人脸检测算法

Dynamic Weights and Pre-partitioning Real-Adaboost Face Detection Algorithm

提出了Real-Adaboost的一种改进算法。该算法采用预先计算类Haar特征所对应弱分类器在样本空间的划分,并动态更新人脸训练样本的权值。与以往的Real-Adaboost算法比较,该算法大大缩短了训练时间,算法训练时间复杂度降到O(T*M*N),同时加速了强分类器的收敛性能,减少检测器的弱分类器数量,减少检测时间。

This paper proposes a novel human face detection algorithm based on real Adaboost algorithm. Policy that calculates in advance the partitioning of Haar-like feature weak classifiers in sample input space and updating training face samples＇ weights dynamically is adopted. This algorithm reduces training time cost greatly compared with classical real-Adaboost algorithm. In addition, it speeds up strong classifier converging, reduces the number of weak classifiers and decreases detecting time.