title: Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis.
abstract: 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.
authors.affiliation: School of Computer, Guangdong University of Technology, Guangzhou, China.
authors.forename: Yunhua
authors.initials: Y
authors.lastname: Chen
authors.affiliation: South China University of Technology, School of Electronic and Information Engineering, Guangzhou, China; VTRON Technology Company, R&D Center, Guangzhou, China.
authors.forename: Weijian
authors.initials: W
authors.lastname: Liu
authors.affiliation: School of Computer, Guangdong University of Technology, Guangzhou, China.
authors.forename: Ling
authors.initials: L
authors.lastname: Zhang
authors.affiliation: State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
authors.forename: Mingyu
authors.initials: M
authors.lastname: Yan
authors.affiliation: Biomedical Engineering Center, Beijing University of Technology, Beijing, China.
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Zeng
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D005143:Eyelids / Q000473:pathology; D005145:Face / Q000473:pathology*; D015673:Fatigue Syndrome, Chronic / Q000175:diagnosis* / Q000503:physiopathology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated; D015595:Skin Aging / Q000473:pathology
keywords: Chronic fatigue syndrome; Feature extraction; Hybrid facial feature; Manifold preserving projection; Non-invasive CFS diagnosis