title: Automatic recognition of facial movement for paralyzed face.
abstract: Facial nerve paralysis is a common disease due to nerve damage. Most approaches for evaluating the degree of facial paralysis rely on a set of different facial movements as commanded by doctors. Therefore, automatic recognition of the patterns of facial movement is fundamental to the evaluation of the degree of facial paralysis. In this paper, a novel method named Active Shape Models plus Local Binary Patterns (ASMLBP) is presented for recognizing facial movement patterns. Firstly, the Active Shape Models (ASMs) are used in the method to locate facial key points. According to these points, the face is divided into eight local regions. Then the descriptors of these regions are extracted by using Local Binary Patterns (LBP) to recognize the patterns of facial movement. The proposed ASMLBP method is tested on both the collected facial paralysis database with 57 patients and another publicly available database named the Japanese Female Facial Expression (JAFFE). Experimental results demonstrate that the proposed method is efficient for both paralyzed and normal faces.
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, Songling Road 268th, Qingdao 266100, China.
authors.forename: Ting
authors.initials: T
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, Songling Road 268th, Qingdao 266100, China.
authors.forename: Junyu
authors.initials: J
authors.lastname: Dong
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, Songling Road 268th, Qingdao 266100, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Sun
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, Songling Road 268th, Qingdao 266100, China.
authors.forename: Shu
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Technology, Ocean University of China, Songling Road 268th, Qingdao 266100, China.
authors.forename: Shengke
authors.initials: S
authors.lastname: Wang
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D002648:Child; D005145:Face / Q000473:pathology* / Q000503:physiopathology*; D005158:Facial Paralysis / Q000175:diagnosis* / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Facial paralysis; active shape models; facial movement; local binary patterns