Optimization of a training set for more robust face detection

doi:10.1016/j.patcog.2009.02.006

	Optimization of a training set for more robust face detection
	Chen, Jie 2; Chen, Xilin 1; Yang, Jie 3; Shan, Shiguang 1; Wang, Ruiping 1; Gao, Wen 1,2
	2009-11-01
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
卷号	42 期号:11 页码:2828-2840
摘要	The performance of a learning-based method highly depends on the quality of a training set. However, it is very challenging to collect an efficient and effective training set for training a good classifier, because of the high dimensionality of the feature space and the complexity of decision boundaries. In this research, we study the methodology of automatically obtaining an optimal training set for robust face detection by resampling the collected training set. We propose a genetic algorithm (GA) and manifold-based method to resample a given training set for more robust face detection. The motivations behind lie in two folds: (1) dynamic optimization, diversity, and consistency of the training samples are cultivated by the evolutionary nature of GA and (2) the desirable non-linearity of the training set is preserved by using the manifold-based resampling. We demonstrate the effectiveness of the Proposed method through experiments and Comparisons to other existing face detectors. The system trained from the training set by the proposed method has achieved 90.73% accuracy with no false alarm on MIT+CMU frontal face test set-the best result reported so far to our knowledge. Moreover, as a fully automatic technology, the proposed method can significantly facilitate the preparation of training sets for obtaining well-performed object detection systems in different applications. (C) 2009 Elsevier Ltd. All rights reserved.
关键词	Resampling Genetic algorithm Manifold Face detection
DOI	10.1016/j.patcog.2009.02.006
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[60533030] ; Natural Science Foundation of China[U0835005] ; Natural Science Foundation of China[60772071] ; National Basic Research Program of China (973 Program)[2009CB320902]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000268453800042
出版者	ELSEVIER SCI LTD
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/11450
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Xilin
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China 3.Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA
推荐引用方式 GB/T 7714	Chen, Jie,Chen, Xilin,Yang, Jie,et al. Optimization of a training set for more robust face detection[J]. PATTERN RECOGNITION,2009,42(11):2828-2840.
APA	Chen, Jie,Chen, Xilin,Yang, Jie,Shan, Shiguang,Wang, Ruiping,&Gao, Wen.(2009).Optimization of a training set for more robust face detection.PATTERN RECOGNITION,42(11),2828-2840.
MLA	Chen, Jie,et al."Optimization of a training set for more robust face detection".PATTERN RECOGNITION 42.11(2009):2828-2840.