Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (15): 34-37.

Previous Articles     Next Articles

Face expression recognition fusion LBP and local sparse representation

TANG Hengliang1,2, SUN Yanfeng1, ZHU Jie2, ZHAO Mingru1,2   

  1. 1.Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing 100124, China
    2.Beijing Key Laboratory of Intelligent Logistics System, Beijing Wuzi University, Beijing 101149, China
  • Online:2014-08-01 Published:2014-08-04

融合LBP和局部稀疏表示的人脸表情识别

唐恒亮1,2,孙艳丰1,朱  杰2,赵明茹1,2   

  1. 1.北京工业大学 多媒体与智能软件技术北京市重点实验室,北京 100124
    2.北京物资学院 智能物流系统北京市重点实验室,北京 101149

Abstract: In order to effectively represent facial expression feature, a novel method fusing Local Binary Pattern(LBP) and local sparse representation is proposed for facial expression representation and recognition. The face image is divided into non-uniform local regions based on face features, and the LBP features are calculated for every facial local region. For depicting facial local texture exactly and integrating facial local features, a facial local reconstruction method based on sparse representation is designed. According to the influence on the facial regions for expression, a weighted fusion algorithm is presented to collect all the local reconstruction residuals and address the expression recognition task. The experiments tested on JAFFE2 face expression database demonstrate the proposed method is feasible and robust to facial expression.

Key words: expression recognition, feature fusion, Local Binary Pattern(LBP), sparse representation

摘要: 为更好获取人脸局部表情特征,提出了一种融合局部二值模式(Local Binary Pattern,LBP)和局部稀疏表示的人脸表情特征与识别方法。为深入分析表情对人脸子区域的影响,根据五官特征对人脸进行非均匀分区,并提取局部LBP特征;为精细刻画人脸局部纹理,整合人脸局部特征,设计了人脸局部稀疏重构表示方法,并根据表情对各局部子区域的影响因子,加权融合局部重构残差进行人脸表情识别。在JAFFE2表情人脸库上的对比实验,验证了该方法的可行性和鲁棒性。

关键词: 表情识别, 特征融合, 局部二值模式, 稀疏表示