一种基于光流双输入网络的微表情顶点帧检测方法

Micro-expression apex frame contains abundant micro-expression information. In order to spot the apex frame accurately, a neural network classification was proposed based on optical flow characteristics. Taking prior knowledge as rules, a detection method was designed to realize micro-expression apex frame spotting. Firstly, optical flow information was extracted from the image in a fixed size sliding window. And then, the spatial and temporal features of optical flow information in x and y directions was extracted and classified based on dual input network. Finally, according to the trade-off rules based on prior knowledge of micro expression, a post-processing was carried out to improve the detection accuracy. The experimental results on data set CASMEⅡtesting show that the apex spotting rate (ASR) and F₁-score can reach up to 0.945 and 0.925 respectively.