Feature Pruning based AdaBoost and its application in face detection

被引：0
|
作者：
Meng, Zi-Bo [1 ]
Jiang, Hong [1 ]
Chen, Jing [1 ]
Yuan, Bo [1 ]
Wang, Li-Qiang [1 ]
机构：
[1] State Key Laboratory of Modern Optical Instrumentation, Zhejiang University
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2013年 / 47卷 / 05期
关键词：
AdaBoost; Confirmation and skipping detection scheme; Face detection; Feature pruning;
D O I：
10.3785/j.issn.1008-973X.2013.05.025
中图分类号：
学科分类号：
摘要：
The AdaBoost algorithm is highly computational consuming and has high false positive rate. To deal with these problems, an efficient detection method based on AdaBoost, which consists of a Feature Pruning based AdaBoost (FPAdaBoost) algorithm and a confirmation and skipping detection scheme (CSDS), is presented in this paper. FPAdaBoost cuts off features at a certain cutting coefficient according to the classification error in each iteration of training process, which effectively speeds up the learning process and greatly reduces the computational cost. And CSDS employs verification and confirmation scheme in the conventional scanning process, which effectively eliminates false positive detections. The performance of proposed detection method was tested in face detection using the MIT-CBCL training set and the MIT+CMU test set. The results show that, compared with traditional Adaboost detection method, the training time of FPAdaBoost dramatically decreases without suffering a decline in classification capability, meanwhile the false positive rate is significantly reduced due to employing CSDS in the scanning process.
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页码：906 / 911
页数：5
相关论文
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