# Evaluating and Correcting Human Annotation Bias in Dynamic Micro-Expression Recognition

arXiv: https://arxiv.org/abs/2603.04766 · HTML: https://mlsanity.com/paper/2603.04766/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 67 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Retentive Network: A Successor to Transformer for Large Language Models](https://mlsanity.com/paper/2307.08621/) (2023)
- [Transformers without Normalization](https://mlsanity.com/paper/2503.10622/) (2025)
- [Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms](https://mlsanity.com/paper/2201.12728/) (2022)
- [Micron-BERT: BERT-based Facial Micro-Expression Recognition](https://mlsanity.com/paper/2304.03195/) (2023)
- [Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods](https://mlsanity.com/paper/1511.00423/) (2015)
- [Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions](https://mlsanity.com/paper/1901.04656/) (2019)
- [MMNet: Muscle motion-guided network for micro-expression recognition](https://mlsanity.com/paper/2201.05297/) (2022)
- [Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition](https://mlsanity.com/paper/2304.04420/) (2023)
- [Micro-expression detection in long videos using optical flow and recurrent neural networks](https://mlsanity.com/paper/1903.10765/) (2019)
- [Spotting Macro- and Micro-expression Intervals in Long Video Sequences](https://mlsanity.com/paper/1912.11985/) (2019)
- [MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution](https://mlsanity.com/paper/2506.14511/) (2025)
- [AU-aware graph convolutional network for Macro- and Micro-expression spotting](https://mlsanity.com/paper/2303.09114/) (2023)
- [Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition](https://mlsanity.com/paper/2310.04664/) (2023)
- [FacialFlowNet: Advancing Facial Optical Flow Estimation with a Diverse Dataset and a Decomposed Model](https://mlsanity.com/paper/2409.05396/) (2024)
- [Objective Micro-Facial Movement Detection Using FACS-Based Regions and Baseline Evaluation](https://mlsanity.com/paper/1612.05038/) (2016)
- [Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition](https://mlsanity.com/paper/2406.08997/) (2024)

## Built on by (most cited first, 2 of 2)

- [MEDN: Motion-Emotion Feature Decoupling Network for Micro-Expression Recognition](https://mlsanity.com/paper/2604.17899/) (2026)
- [SAC2-Net: Semantic Anchoring and Complementary-Consensus Fusion for Multimodal Micro-Expression Recognition](https://mlsanity.com/paper/2606.25542/) (2026)

## Abstract (arXiv)

Existing manual labeling of micro-expressions is subject to errors in accuracy, especially in cross-cultural scenarios where deviation in labeling of key frames is more prominent. To address this issue, this paper presents a novel Global Anti-Monotonic Differential Selection Strategy (GAMDSS) architecture for enhancing the effectiveness of spatio-temporal modeling of micro-expressions through keyframe re-selection. Specifically, the method identifies Onset and Apex frames, which are characterized by significant micro-expression variation, from complete micro-expression action sequences via a dynamic frame reselection mechanism. It then uses these to determine Offset frames and construct a rich spatio-temporal dynamic representation. A two-branch structure with shared parameters is then used to efficiently extract spatio-temporal features. Extensive experiments are conducted on seven widely recognized micro-expression datasets. The results demonstrate that GAMDSS effectively reduces subjective errors caused by human factors in multicultural datasets such as SAMM and 4DME. Furthermore, quantitative analyses confirm that offset-frame annotations in multicultural datasets are more uncertain, providing theoretical justification for standardizing micro-expression annotations. These findings directly support our argument for reconsidering the validity and generalizability of dataset annotation paradigms. Notably, this design can be integrated into existing models without increasing the number of parameters, offering a new approach to enhancing micro-expression recognition performance. The source code is available on GitHub[https://github.com/Cross-Innovation-Lab/GAMDSS].