摘要
针对AdaBoost算法忽略弱分类器之间的相关性导致强分类器的集成性能降低的缺陷,提出一种改进的AdaBoost人脸检测算法。通过加入差异性度量进行相关性判定,并根据判定值剔除相似特征,以有效增加分类器的多样性。实验结果表明,相同条件下,改进的算法提高了人脸检测率,降低了错误检测数。
Aiming at the problem of traditional AdaBoost algorithm ignoring the correlation between weak classifiers,which reduces the ensemble of the AdaBoost algorithm,an improved AdaBoost face detection algorithm is proposed.The algorithm uses the disagreement measure as the degrees of diversity between classifiers and eliminates similar features according to de-cision value,so as to effectively improve the weak classifier diversity.Experimental results show that the algorithm im-proves the face detection rate and reduces false alarm rate in the same conditions.
出处
《桂林电子科技大学学报》
2015年第3期213-216,共4页
Journal of Guilin University of Electronic Technology
基金
国家自然科学基金(61362007)
关键词
差异性度量
相关性
人脸检测
AdaBoost
AdaBoost
disagreement measure
correlation
face detection
