# DFME: A New Benchmark for Dynamic Facial Micro-expression Recognition

arXiv: https://arxiv.org/abs/2301.00985 · HTML: https://mlsanity.com/paper/2301.00985/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 81 references that are in the map.
- First posted on arXiv 3 Jan 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://mlsanity.com/paper/1905.00641/) (2019)
- [DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild](https://mlsanity.com/paper/2008.05924/) (2020)
- [Style Aggregated Network for Facial Landmark Detection](https://mlsanity.com/paper/1803.04108/) (2018)
- [Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms](https://mlsanity.com/paper/2201.12728/) (2022)
- [OFF-ApexNet on Micro-expression Recognition System](https://mlsanity.com/paper/1805.08699/) (2018)
- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition](https://mlsanity.com/paper/1902.03634/) (2019)
- [Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods](https://mlsanity.com/paper/1511.00423/) (2015)
- [Less is More: Micro-expression Recognition from Video using Apex Frame](https://mlsanity.com/paper/1606.01721/) (2016)
- [Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition](https://mlsanity.com/paper/2006.09674/) (2020)
- [Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition](https://mlsanity.com/paper/1805.08417/) (2018)
- [Cross-Database Micro-Expression Recognition: A Benchmark](https://mlsanity.com/paper/1812.07742/) (2018)
- [Data Leakage and Evaluation Issues in Micro-Expression Analysis](https://mlsanity.com/paper/2211.11425/) (2022)

## Built on by (most cited first, 13 of 13)

- [Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition](https://mlsanity.com/paper/2409.09707/) (2024)
- [MELLM: A Flow-Guided Large Language Model for Micro-Expression Understanding](https://mlsanity.com/paper/2505.07007/) (2025)
- [FED-PsyAU: Privacy-Preserving Micro-Expression Recognition via Psychological AU Coordination and Dynamic Facial Motion Modeling](https://mlsanity.com/paper/2507.20557/) (2025)
- [Hierarchical Space-Time Attention for Micro-Expression Recognition](https://mlsanity.com/paper/2405.03202/) (2024)
- [Temporal and Spatial Feature Fusion Framework for Dynamic Micro Expression Recognition](https://mlsanity.com/paper/2505.16372/) (2025)
- [ME-TST+: Micro-expression Analysis via Temporal State Transition with ROI Relationship Awareness](https://mlsanity.com/paper/2508.08082/) (2025)
- [MELDAE: A Framework for Micro-Expression Spotting, Detection, and Automatic Evaluation in In-the-Wild Conversational Scenes](https://mlsanity.com/paper/2510.22575/) (2025)
- [An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition](https://mlsanity.com/paper/2310.10022/) (2023)
- [Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression Recognition](https://mlsanity.com/paper/2406.17538/) (2024)
- [A Benchmark for Incremental Micro-expression Recognition](https://mlsanity.com/paper/2501.19111/) (2025)
- [MMME: A Spontaneous Multi-Modal Micro-Expression Dataset Enabling Visual-Physiological Fusion](https://mlsanity.com/paper/2506.09834/) (2025)
- [CDER-SME: A Cross-Device Event-RGB Micro-Expression Dataset under Multi-Level Stress Induction](https://mlsanity.com/paper/2606.20715/) (2026)
- [SAC2-Net: Semantic Anchoring and Complementary-Consensus Fusion for Multimodal Micro-Expression Recognition](https://mlsanity.com/paper/2606.25542/) (2026)

## Abstract (arXiv)

One of the most important subconscious reactions, micro-expression (ME), is a spontaneous, subtle, and transient facial expression that reveals human beings' genuine emotion. Therefore, automatically recognizing ME (MER) is becoming increasingly crucial in the field of affective computing, providing essential technical support for lie detection, clinical psychological diagnosis, and public safety. However, the ME data scarcity has severely hindered the development of advanced data-driven MER models. Despite the recent efforts by several spontaneous ME databases to alleviate this problem, there is still a lack of sufficient data. Hence, in this paper, we overcome the ME data scarcity problem by collecting and annotating a dynamic spontaneous ME database with the largest current ME data scale called DFME (Dynamic Facial Micro-expressions). Specifically, the DFME database contains 7,526 well-labeled ME videos spanning multiple high frame rates, elicited by 671 participants and annotated by more than 20 professional annotators over three years. Furthermore, we comprehensively verify the created DFME, including using influential spatiotemporal video feature learning models and MER models as baselines, and conduct emotion classification and ME action unit classification experiments. The experimental results demonstrate that the DFME database can facilitate research in automatic MER, and provide a new benchmark for this field. DFME will be published via https://mea-lab-421.github.io.