一组用于快速人脸检测的分开Haar特征

本论文提出了一种能快速、精准用于人脸检测的特征即分开Haar特征（Separate Haar,简称Sep-Haar）.本文研究过程中有3个关键贡献,第一是提出"分开Haar特征",即在Haar特征矩形之间添加了一个不关心的区域,可通过这个算法得到一些更有效的特征.第二是为这个不关心区域选择最好宽度的算法,这个算法用于减少学习特征的总数量,以减少内存的使用.第三是同样通过Adaboost算法应用,采用Sep-Haar特征能使用较少量的特征而得到最好的误报率.基于此研究结果,本文也提出了一种新分类器,每个阶段都有较小的误报率,实验结果表明使用该特征能够在减少检测时间情况下提高命中率.

A Set of Separate Haar Features for Rapid Face Detection

In this paper, we describe a new feature called Separate Haar (Sep-Haar) feature for fast and accurate face detection. There are three key contributions. "Separate Haar feature" adds a negligible area for the rectangular Haar feature window, by which we can improve the feature extraction efficiency; the corresponding algorithm for selecting the best width of such negligible area is realized by reducing the total number of learned features to reduce the memory used; and experiment result shows that the proposed Sep-Haar feature can achieve best false alarm rate using less number of features in Adaboost algorithm compared with traditional Haar feature. Based on the result, we propose a new classifier that, by using the proposed Sep-Haar features, it can give smaller false alarm rate at each stage, use less number of stages, and at the same time give improved hit rate within the same detection time consummed.