# Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos Source: [Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:16:37.291Z Authors: Gen-Bing Liong, John See, Lai-Kuan Wong Published: 2021-06-11T16:19:48.000Z ## Abstract Facial expressions vary from the visible to the subtle. 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 microexpressions 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Facial expression spotting on SAMM-LV](): Facial Expression Spotting; SAMM-LV; ME F1-score: 15.2. - [Facial Expression Spotting on CAS(ME)2](): Facial Expression Spotting; CAS(ME)2; Overall F1-score: 20.22. ## Original sources and implementation [Original paper]() [PDF]()