摘要： 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。

关键词: 人脸检测, 实值Adaboost, 类Haar特征, 层叠分类器, 动态权值

Abstract: 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.

Key words: Face detection, Real-Adaboost, Haar-like feature, Cascade classifier, Dynamic weight