融合时空特征的视频序列表情识别

摘要

For facial expression recognition based on video sequences,the changing information of facial regions along the time axis can be described by dynamic descriptors more effectively than static descriptors.This paper proposes an expression recognition method based on the dynamic texture and motion information,learning from the principle of Local Binary Pattern on Three Orthogonal Planes (LBP-TOP),Spatio-Temporal Weber Local Descriptor (STWLD) is proposed to describe the dynamic texture feature information of the facial expression sequence.Moreover,using Block-based Histogram of Optical Flow features (BHOF),the motion information can be described.Through the combination of the dynamic texture and motion information,and finally SVM is applied to complete the expression classification.The results of the cross experiments on the CK + and MMI expression database show that the method achieves better performance than methods using the single descriptors.The comparison experiments with other related methods also prove the superiority of the method.%针对视频表情识别,静态特征不能有效描述人脸区域沿时间轴动态变化信息的局限,该文提出一种融合动态纹理信息和运动信息的表情识别方法,借鉴LBP-TOP原理,提出具有时空域描述能力的时空韦伯局部描述子(STWLD)来提取动态纹理信息,同时采用分块光流直方图(BHOF)描述运动信息,最后利用SVM对融合后的纹理和运动信息完成表情分类.在CK+和MMI表情数据库上的交叉实验结果表明,相比基于单一特征的识别方法,所提方法取得了更好的效果;与其他相关方法的对比实验也验证了该方法的优越性.