摘要：

为提高传统AdaBoost算法的集成性能，降低算法复杂度，提出2种基于分类器相关性的AdaBoost算法。在弱分类器的训练过程中，加入Q统计量进行判定。每个弱分类器的权重更新不仅与当前分类器有关，而且需要考虑到前面的若干分类器，以有效降低弱分类器间的相似性，剔除相似特征。仿真结果表明，该算法具有更好的检测率，同时可降低误检率，改进分类器的整体性能。

关键词: 人脸检测, 分类器相关性, 自适应提升算法, Q统计量

Abstract:

In order to enhance the ensemble of the traditional AdaBoost algorithm and reduce its complexity, two improved AdaBoost algorithms are proposed, which are based on the correlation of classifiers. In the algorithm, Q-statistic is added in the training weak classifiers. Every weak classifier is related not only to the current classifier, but also to the previous classifiers as well, which can effectively reduce the weak classifier similarity. Simulations show that the algorithm is of better detection rate and lower false alarm rate.

Key words: face detection, correlation of classifiers, adaptive boosting algorithm, Q-statistic

中图分类号: 

• TP391.4