Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (19): 173-177.

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AdaBoost face detection algorithm based on optimized weighting parameter

MIAO Danquan, ZHENG Herong, GU Guomin   

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
  • Online:2014-10-01 Published:2014-09-29

基于优化加权参数的AdaBoost人脸检测算法

缪丹权,郑河荣,顾国民   

  1. 浙江工业大学 计算机科学与技术学院,杭州 310023

Abstract: Existed single threshold methods will cause highly false recognition rates in face detection. This paper proposes a fast AdaBoost algorithm based on optimized weighting parameter. Firstly, algorithm changes the solving formula of weighted parameter to get low false alarm rates even under the premise of low false recognition rates. Secondly, dual-threshold is obtained by calculating the feature-value curve. Finally, the detected dual thresholds are used to form weak classifiers, which can form an ensemble classifier. Experimental results show that it not only improves the accuracy of detection, but also ameliorates the training and detecting time since dual-threshold can decrease the times of searching.

Key words: face detection, Haar-like feature, AdaBoost algorithm, dual-threshold, optimized weighting parameter

摘要: 针对在已有人脸检测方法中采用单阈值所导致的误检率太高的问题,提出一种基于优化加权参数的快速AdaBoost训练检测算法。算法通过改变弱分类器加权参数求解公式的方法,保证了在低误检率的前提下也能获得低误警率;通过特征值曲线自适应得到双阈值,然后构造双阈值弱分类器并进行集成,形成强分类器。实验结果表明,该算法不仅能够有效地提高检测精度,而且,由于双阈值能够减少搜索次数,从而使训练和检测时间也有明显的改进。

关键词: 人脸检测, Haar-like特征, AdaBoost算法, 双阈值, 优化加权参数