Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis

doi:10.1016/j.compbiomed.2015.06.005

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis
	Chen, Yunhua 5; Liu, Weijian 1,4; Zhang, Ling 5; Yan, Mingyu 3; Zeng, Yanjun 2
刊名	COMPUTERS IN BIOLOGY AND MEDICINE
	2015-09-01
卷号	64 页码:30-39
关键词	Chronic fatigue syndrome Feature extraction Hybrid facial feature Manifold preserving projection Non-invasive CFS diagnosis
ISSN号	0010-4825
DOI	10.1016/j.compbiomed.2015.06.005
英文摘要	Due to an absence of reliable biochemical markers, the diagnosis of chronic fatigue syndrome (CFS) mainly relies on the clinical symptoms, and the experience and skill of the doctors currently. To improve objectivity and reduce work intensity, a hybrid facial feature is proposed. First, several kinds of appearance features are identified in different facial regions according to clinical observations of traditional Chinese medicine experts, including vertical striped wrinkles on the forehead, puffiness of the lower eyelid, the skin colour of the cheeks, nose and lips, and the shape of the mouth corner. Afterwards, such features are extracted and systematically combined to form a hybrid feature. We divide the face into several regions based on twelve active appearance model (AAM) feature points, and ten straight lines across them. Then, Gabor wavelet filtering, CIELab color components, threshold-based segmentation and curve fitting are applied to extract features, and Gabor features are reduced by a manifold preserving projection method. Finally, an AdaBoost based score level fusion of multi-modal features is performed after classification of each feature. Despite that the subjects involved in this trial are exclusively Chinese, the method achieves an average accuracy of 89.04% on the training set and 88.32% on the testing set based on the K-fold cross-validation. In addition, the method also possesses desirable sensitivity and specificity on CFS prediction. (C) 2015 Elsevier Ltd. All rights reserved.
资助项目	National Natural Science Foundation of Guangdong, China[2014A030310169] ; Science and Technology Program of Guangzhou, China[2014Y2-00211]
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000361412500004
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/9326]
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, Yunhua
作者单位	1.VTRON Technol Co, R&D Ctr, Guangzhou, Guangdong, Peoples R China 2.Beijing Univ Technol, Ctr Biomed Engn, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China 4.S China Univ Technol, Sch Elect & Informat Engn, Guangzhou, Guangdong, Peoples R China 5.Guangdong Univ Technol, Sch Comp, Guangzhou, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Chen, Yunhua,Liu, Weijian,Zhang, Ling,et al. Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2015,64:30-39.
APA	Chen, Yunhua,Liu, Weijian,Zhang, Ling,Yan, Mingyu,&Zeng, Yanjun.(2015).Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis.COMPUTERS IN BIOLOGY AND MEDICINE,64,30-39.
MLA	Chen, Yunhua,et al."Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis".COMPUTERS IN BIOLOGY AND MEDICINE 64(2015):30-39.