基于Adaboost算法的人脸检测

该文提出了一种基于改进的Adaboost算法的人脸检测方法.Adaboost是一种构建准确分类器的学习算法,它将一族弱学习算法通过一定规则结合成为一个强学习算法,从而通过样本训练得到一个识别准确率理想的分类器.但是,Adaboost在有高噪音样本的情况下,有可能发生过配现象,该文在Adaboost算法的基础上,对其权值更新规则做了改进,并结合PCA进行人脸检测.仿真试验表明,该方法具有良好的性能,同时可以在一定程度上有效防止过配现象的发生.

Face Detection Based on Adaboost Algorithm

This paper presents a new algorithm based on improved Adaboost for face detection.Adaboost is a learning algorithm for constructing accurate classifiers.It can obtain a strong learning algorithm by combining a series of weak learning algorithms through some rules,but it tends to overfit in the presence of highly noise samples.In this paper,we improve the weigh-update rules,and use PCA and our improved Adaboost to detect face images.Experiment results show that this approach has good performance,and the modified weigh-update rules can effectively avoid overfitting.