[1]PENG Cheng,LIU Shuaishi,WAN Chuan,et al.An active shape model for facial expression recognition based on a local texture model[J].CAAI Transactions on Intelligent Systems,2011,6(3):231-238.
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An active shape model for facial expression recognition based on a local texture model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 6 Number of periods: 2011 3 Page number: 231-238 Column: 学术论文—机器感知与模式识别 Public date: 2011-06-25
Title:
An active shape model for facial expression recognition based on a local texture model
Author(s):
PENG Cheng1;  LIU Shuaishi1;  WAN Chuan1;  TIAN Yantao1; 2
1.School of Communication Engineering, Jilin University, Changchun 130025, China;
?2. Key Laboratory of Bionic Engineering (Jilin University), Ministry of Education, Changchun 130025, China
Keywords:
facial expression recognition;  active shape model;  local texture model;  RBF neural network classifier
CLC:
TP391
DOI:
-
Abstract:
An improved active shape model(ASM) called EWASM (expanded weighted ASM) based on a local texture model was proposed because EWASM overcomes the disadvantage that the active shape model is easy to involve in local optimal solution in the iterative process. In the local texture model, searching adjacent information of each landmark along its perpendicular bisector made the match position best. It improved and promoted Mahalanobis distance which measured the matching degree. Then the local texture model was extended to include the center local texture model, forward local texture model, and backward local texture model. After that, the weighted parameters were optimized experimentally. Thus each landmark is more closely related and the local texture model is more robust. Finally facial expression recognition experiments were conducted comparing EWASM with classical ASM, and a RBF neural network was used as a classification in the expression recognition. Experiments show that the EWASM algorithm solved the local minimum problem and achieved a better convergence rate and recognition effect. 
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