Abstract:

In order to reveal the correlation between the micro-expression categories and the local regions, the method for partitioning and analyzing local region is proposed. Firstly, based on the location of action units related to various micro-expressions, seven crucial local regions of face are determined. Then, 49 facial point coordinates are detected automatically through facial deformation identification model and the four boundaries of the aforementioned local regions are established according to these coordinates. The spatio-temporal pattern features of each region are extracted and the micro-expressions are classified. Finally, the correlation between the micro-expression categories and the local regions are obtained by the recognition accuracy of each region for every micro-expression category. The experimental results show that the proposed scheme for partitioning local region is precise and reasonable; there is a highly correlation between the eye area and surprise or disgust. The similar correlations also lie in the mouth area and the happiness, the chin area and the depression.

Key words: micro-expression recognition, local region, action units, facial deformation identification model, recognition accuracy

摘要：

为了揭示微表情类别与面部各区域之间的关联度，提出一种局部区域划分和分析方法。该方法首先根据各类微表情对应动作单元所在的位置确定出人脸中最为关键的7个局部区域，然后采用面部形变鉴别模型自动检测出面部49个关键点坐标，再根据这些点确定上述局部区域的4个边界。最后，分别提取各区域的时空模式特征并进行微表情分类，通过各区域对各类微表情的分类准确率来分析两者之间的关联度。实验结果表明，局部区域划分方案科学合理，微表情“惊奇”及“厌恶”与眼睛区域、微表情“高兴”与嘴巴区域、微表情“压抑”与下巴区域有较高的关联度。

关键词: 微表情识别, 局部区域, 动作单元, 面部形变鉴别模型, 分类准确率