Radial-curve-based facial expression recognition  

作　　者：岳雷 沈庭芝 张超 赵三元 杜部致 

机构地区：[1]School of Information and Electronics,Beijing Institute of Technology [2]School of Computer Science and Technology,Beijing Institute of Technology

出　　处：《Journal of Beijing Institute of Technology》2015年第4期508-512,共5页北京理工大学学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(60772066)

摘　　要：A fully automatic facial-expression recognition （FER） system on 3D expression mesh models was proposed. The system didn＇ t need human interaction from the feature extraction stage till the facial expression classification stage. The features extracted from a 3D expression mesh mod- el were a bunch of radial facial curves to represent the spatial deformation of the geometry features on human face. Each facial curve was a surface line on the 3D face mesh model, begun from the nose tip and ended at the boundary of the previously trimmed 3D face points cloud. Then Euclid dis- tance was employed to calculate the difference between facial curves extracted from the neutral face and each face with different expressions of one person as feature. By employing support vector ma- chine （SVM） as classifier, the experimental results on the well-known 3D-BUFE dataset indicate that the proposed system could better classify the six prototypical facial expressions than state-of-art al- gorithms.A fully automatic facial-expression recognition （FER） system on 3D expression mesh models was proposed. The system didn＇ t need human interaction from the feature extraction stage till the facial expression classification stage. The features extracted from a 3D expression mesh mod- el were a bunch of radial facial curves to represent the spatial deformation of the geometry features on human face. Each facial curve was a surface line on the 3D face mesh model, begun from the nose tip and ended at the boundary of the previously trimmed 3D face points cloud. Then Euclid dis- tance was employed to calculate the difference between facial curves extracted from the neutral face and each face with different expressions of one person as feature. By employing support vector ma- chine （SVM） as classifier, the experimental results on the well-known 3D-BUFE dataset indicate that the proposed system could better classify the six prototypical facial expressions than state-of-art al- gorithms.

关 键 词：facial expression radial curve Euclidean distance support vector machine （SVM） 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]