基于AdaBoost人脸检测算法中的特征筛选方法

文章从机器视觉的发展历程出发，首先介绍了人脸检测的概念原理。接下来在引入AdaBoost算法后，详细阐述了算法中的关于积分图、特征值的计算方法以及强分类器的具体训练过程。在对训练效率进行科学分析后，文章重点介绍了一种基于降低错误率的贡献度的特征筛选方法，以减少垃圾特征对系统的资源的消耗和不良的影响。最后给出人脸检测的实验结果并得出结论。

Feature selection method in Face Detection based on AdaBoost algorithm

This article began with the machine vision development process starting, first introduced the concept of face detection. Then, after the introduction of AdaBoost algorithm, the paper described in detail on the the calculation of points chart, eigenvalues and the strong classifier specific training process. After the scientific analysis of the efficiency in the training, the article focused on reducing error rates based on the contribution of feature selection methods to reduce the rubbish features of the system resource consumption and adverse effects. Finally, the experimental results of face detection and a conclusion were reached.