Aggregation of Motion Features of Multiple Paths for Micro-Expression Recogni-tion
She Wenxiang1,2), Liu Bin2), Tao Jianhua1,2)*, Zhang Hao1,2), and Lyu Zhao1)
1) (Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601)2) (National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190)
Since the change of micro-expression is not obvious, subtle features are easily lost in the learning process, which limits the performance of the model. A micro-expression recognition method is proposed based on motion features. Firstly, in order to explore the influence of regions with obvious change on micro-expression recognition, the relatively active regions of micro-expression are locally cut according to physiological research, and the features of local region and global region are extracted respectively by using parallel neural network. When extracting features, a motion feature extraction module which can extract feature level motion information is adopted. The module can learn motion features from spatial feature graph, and aggregate motion features and spatial features to reduce the loss of subtle features. Finally, the local aggregate features and global aggregate features are combined to form a new hybrid feature for micro-expression recognition. Experiments on the MEGC 2019 (including CASME II, SMIC, and SAMM) databases show that the results of unweighted F1 score and unweighted average recall are 81.81% and 79.01%, which are improved by 2.96% and 0.77% respectively compared with the best method of MEGC 2019.