Research on Micro-Expression Spotting Method Based on Optical Flow Features

This paper aims to propose an automatic micro-expression spotting method of high accuracy and high robustness. Due to the characteristics of small amplitude and short duration, how to accurately capture the subtle movements of micro-expression is a complex problem. The optical flow method is applied to estimate the motion trend of the facial regions. Because the head shaking is an essential reason for the high false-positive rate of micro-expression spotting, a reliable face alignment method becomes crucial. According to the optical flow of the nose tip region, the cutting box was adjusted several times to optimize the relative position between the face and the cutting box stable. On this basis, the optical flow features from the 14 regions of interest on the face are used to build a feature matrix, and a wave peak location technology is proposed to accurately locate the moment when the micro-expression occurs on the time-domain curve of the features. The experimental results on the CAS(ME)2-cropped and the SAMM Long Videos datasets show that our method performs significantly better than the baseline method and has a high application value in various application scenarios.