# Spotting Macro- and Micro-expression Intervals in Long Video Sequences

arXiv: https://arxiv.org/abs/1912.11985 · HTML: https://mlsanity.com/paper/1912.11985/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 12 of 12)

- [3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame](https://mlsanity.com/paper/2105.06340/) (2021)
- [Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results](https://mlsanity.com/paper/2106.06583/) (2021)
- [Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition](https://mlsanity.com/paper/2409.09707/) (2024)
- [Weakly-supervised Micro- and Macro-expression Spotting Based on Multi-level Consistency](https://mlsanity.com/paper/2305.02734/) (2023)
- [Multi-Scale Spatio-Temporal Graph Convolutional Network for Facial Expression Spotting](https://mlsanity.com/paper/2403.15994/) (2024)
- [SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting](https://mlsanity.com/paper/2407.20799/) (2024)
- [Causal-Ex: Causal Graph-based Micro and Macro Expression Spotting](https://mlsanity.com/paper/2503.09098/) (2025)
- [Weak Supervision with Arbitrary Single Frame for Micro- and Macro-expression Spotting](https://mlsanity.com/paper/2403.14240/) (2024)
- [Evaluating and Correcting Human Annotation Bias in Dynamic Micro-Expression Recognition](https://mlsanity.com/paper/2603.04766/) (2026)
- [ME-TST+: Micro-expression Analysis via Temporal State Transition with ROI Relationship Awareness](https://mlsanity.com/paper/2508.08082/) (2025)
- [Boosting Micro-Expression Analysis via Prior-Guided Video-Level Regression](https://mlsanity.com/paper/2508.18834/) (2025)
- [PESFormer: Boosting Macro- and Micro-expression Spotting with Direct Timestamp Encoding](https://mlsanity.com/paper/2410.18695/) (2024)

## Abstract (arXiv)

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-MEGC2020spotting.