Classification of facial images using Gaussian mixture models

	Classification of facial images using Gaussian mixture models
	Liao, P ; Gao, W ; Shen, L ; Chen, XL ; Shan, SG ; Zeng, WB
	2001
发表期刊	ADVANCES IN MUTLIMEDIA INFORMATION PROCESSING - PCM 2001, PROCEEDINGS
ISSN	0302-9743
卷号	2195 页码:724-731
摘要	We present a new technique for face recognition, Two distinct and mutually exclusive classes of difference between two facial images are defined: within-class differences set (differences in appearance of the same individual) and between-class diffierences set (differences in appearance between different individuals). Then Gaussian mixture models (GMMs) are used to estimate the eigenspace densities of the two classes. And subsequently a matching similarity measure is computed based on the maximum likelihood (ML) method. The new method achieved as much as 45% error reduction compared to the standard eigenface approach on the ORL database.
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000174348000093
出版者	SPRINGER-VERLAG BERLIN
引用统计	被引频次：2[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/2XEOYT63/13421
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liao, P
作者单位	1.Chinese Acad Sci, ICT, YCNC, FRTJDL, Beijing 100080, Peoples R China 2.Chinese Acad Sci, Comp Technol Inst, Beijing 100080, Peoples R China 3.Harbin Inst Technol, Dept Comp Sci, Harbin 150001, Peoples R China 4.YCNC Co, Chengdu 610016, SiChuan, Peoples R China
推荐引用方式 GB/T 7714	Liao, P,Gao, W,Shen, L,et al. Classification of facial images using Gaussian mixture models[J]. ADVANCES IN MUTLIMEDIA INFORMATION PROCESSING - PCM 2001, PROCEEDINGS,2001,2195:724-731.
APA	Liao, P,Gao, W,Shen, L,Chen, XL,Shan, SG,&Zeng, WB.(2001).Classification of facial images using Gaussian mixture models.ADVANCES IN MUTLIMEDIA INFORMATION PROCESSING - PCM 2001, PROCEEDINGS,2195,724-731.
MLA	Liao, P,et al."Classification of facial images using Gaussian mixture models".ADVANCES IN MUTLIMEDIA INFORMATION PROCESSING - PCM 2001, PROCEEDINGS 2195(2001):724-731.