LSSNet
Wang-Wang Yu, Jingwen Jiang, Yongjie Li
Abstract
Macro- and micro-expression spotting is a very challenging task to locate their occurrence intervals in long face videos. In this paper, we propose an efficient two-stream network named location suppression based spotting network (LSSNet), which includes three parts. First, the optical flow is extracted using the traditional TV-L1 algorithm which captures subtle facial movements while adding temporal information to alleviate the problem of insufficient samples. Then, fixed length features are extracted from the sampled optical flow and raw images by an I3D model, which is used to set sliding windows. Finally, location suppression modules (LSMs) are added to the pyramidal convolutional neural network (CNN) to reduce the proposals with too long and too short intervals. In addition, we use two different methods, named top_k and top_threshold, for validation. We adopt leave-one-subject-out (LOSO) to train our model on CAS(ME)2 and SAMM-LV. Experimental results show that our LSSNet achieves the state-of-the-art result with top_threshold, especially on the CAS(ME)2 dataset. The code is available at https://github.com/williamlee91/mer_spot.