基于多阈值Boosting方法的人脸检测  

Face Detection Based on Multiple Thresholds Boosting Method

作　　者：钟向阳[1] 凌捷[2] 

机构地区：[1]嘉应学院计算机系,梅州514015 [2]广东工业大学计算机学院,广州510090

出　　处：《计算机工程》2009年第11期172-174,共3页Computer Engineering

基　　金：广东省科技计划基金资助项目(2007B010200071;2006B11201014)

摘　　要：Adaboost算法采用单阈值弱分类器,难以拟合复杂分布,其训练过程收敛速度较慢。针对该问题设计一种多阈值弱学习器,利用平方和减少最大化准则划分节点并生成弱分类器,在训练数据集上采用GAB算法将弱分类器提升为强分类器。实验结果表明,在弱分类器数目相同的情况下,该方法的正样本误报率低于Adaboost算法。Aiming at the problem that the Adaboost algorithm using simple threshold weak classifiers is too weak to fit complex distributions and its slow convergence rate in training, this paper designs a multiple thresholds weak learner. This learner splits the nodes by using biggest reduction in the sum of squares as the partition criteria and builds a weak classifier. It boosts weak classifiers using GAB algorithm on training dataset. Experimental results show that the false positive rate of this method is lower than Adaboost algorithm at the same number of weak classifiers.

关 键 词：人脸检测 BOOSTING方法 实值Adaboost 平缓Adaboost 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]