# Spotting Macro- and Micro-expression Intervals in Long Video Sequences Source: [Spotting Macro- and Micro-expression Intervals in Long Video Sequences]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:09:20.078Z Authors: Ying He, Su-Jing Wang, Jingting Li, Moi Hoon Yap Published: 2019-12-18T06:04:22.000Z ## Abstract This paper presents baseline results for the Third Facial Micro-Expression Grand Challenge (MEGC 2020). Both macro- and micro-expression intervals in CAS(ME)$^2$ and SAMM Long Videos are spotted by employing the method of Main Directional Maximal Difference Analysis (MDMD). The MDMD method uses the magnitude maximal difference in the main direction of optical flow features to spot facial movements. The single-frame prediction results of the original MDMD method are post-processed into reasonable video intervals. The metric F1-scores of baseline results are evaluated: for CAS(ME)$^2$, the F1-scores are 0.1196 and 0.0082 for macro- and micro-expressions respectively, and the overall F1-score is 0.0376; for SAMM Long Videos, the F1-scores are 0.0629 and 0.0364 for macro- and micro-expressions respectively, and the overall F1-score is 0.0445. The baseline project codes are publicly available at https://github.com/HeyingGithub/Baseline-project-for-MEGC2020\_spotting. ## 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: 3.64. - [Facial Expression Spotting on CAS(ME)2](): Facial Expression Spotting; CAS(ME)2; Overall F1-score: 3.76. ## Original sources and implementation [Original paper]() [PDF]()