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Paper Detail

Paper IDARS-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
SessionARS-9: Interpretation, Understanding, Retrieval
LocationArea 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.