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。%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.
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