| 基于扩展Haar特征的AdaBoost人脸检测算法 |
| |
| 引用本文： | 颜学龙,任文帅,马峻.基于扩展Haar特征的AdaBoost人脸检测算法[J].计算机系统应用,2015,24(9):152-155. |
| |
| 作者姓名： | 颜学龙 任文帅 马峻 |
| |
| 作者单位： | 桂林电子科技大学, 桂林 541004;桂林电子科技大学, 桂林 541004;桂林电子科技大学, 桂林 541004 |
| |
| 摘 要： | 对于常用的基于Haar特征的AdaBoost人脸检测算法存在漏检率与误检率高等不足, 增加了Haar特征的扩展种类, 这些新增Haar特征能够有效减少因眉毛与眼睛灰度值近似而引起的误判, 同时去除一些针对人脸分辨效果不好的特征来提高算法的实时性, 深入分析了利用Haar特征与AdaBoost算法构成的级联分类器的特点. 实验数据结果验证了改进后算法的可行性.
|
| 关 键 词： | AdaBoost Haar特征 级联分类器 |
| 收稿时间： | 2014/12/29 0:00:00 |
| 修稿时间： | 2015/3/19 0:00:00 |
AdaBoost Algorithm for Face Detection Based on Extended Haar Feature |
| |
| YAN Xue-Long,REN Wen-Shuai and MA Jun.AdaBoost Algorithm for Face Detection Based on Extended Haar Feature[J].Computer Systems& Applications,2015,24(9):152-155. |
| |
| Authors: | YAN Xue-Long REN Wen-Shuai and MA Jun |
| |
| Affiliation: | Guilin University of Electronic Technology, Guilin 541004, China;Guilin University of Electronic Technology, Guilin 541004, China;Guilin University of Electronic Technology, Guilin 541004, China |
| |
| Abstract: | Aiming at the high undetected rate and false detection rate, and other less which are existed in the AdaBoost algorithm based on Haar feature for face detection, the expanded categories of Haar features are added in this paper, and it can effectively reduce the erroneous judgement caused by the approximation of the gray value between the eyebrows and eyes. At the same time, the real-time of algorithm is improved by removing some features having bad effect for face detection. The cascade classifier constituting of Haar feature and AdaBoost algorithm is analyzed in depth. Finally, the experimental results verify the feasibility of the improved algorithm. |
| |
| Keywords: | AdaBoost Haar feature cascade classifier |
|
| 点击此处可从《计算机系统应用》浏览原始摘要信息 |
|
点击此处可从《计算机系统应用》下载全文 |
|
