Paper ID | ARS-9.3 | ||
Paper Title | SHALLOW OPTICAL FLOW THREE-STREAM CNN FOR MACRO- AND MICRO-EXPRESSION SPOTTING FROM LONG VIDEOS | ||
Authors | Gen-Bing Liong, Multimedia University, Malaysia; John See, Heriot-Watt University Malaysia, Malaysia; Lai-Kuan Wong, Multimedia University, Malaysia | ||
Session | ARS-9: Interpretation, Understanding, Retrieval | ||
Location | Area I | ||
Session Time: | Tuesday, 21 September, 13:30 - 15:00 | ||
Presentation Time: | Tuesday, 21 September, 13:30 - 15:00 | ||
Presentation | Poster | ||
Topic | Image and Video Analysis, Synthesis, and Retrieval: Image & Video Interpretation and Understanding | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
Abstract | In recent years, the analysis of micro-expressions--- a natural occurrence resulting from the suppression of one's true emotions, has drawn the attention of researchers with a broad range of potential applications. However, spotting micro-expressions in long videos becomes increasingly challenging when intertwined with normal or macro-expressions. In this paper, we propose a shallow optical flow three-stream CNN (SOFTNet) model to predict a score that captures the likelihood of a frame being in an expression interval. By fashioning the spotting task as a regression problem, we introduce pseudo-labeling to facilitate the learning process. We demonstrate the efficacy and efficiency of the proposed approach on the recent MEGC 2020 benchmark, where state-of-the-art performance is achieved on CAS(ME)^2 with equally promising results on SAMM Long Videos. |