# Objective Classes for Micro-Facial Expression Recognition

arXiv: https://arxiv.org/abs/1708.07549 · HTML: https://mlsanity.com/paper/1708.07549/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2017.

## Built on by (most cited first, 14 of 14)

- [The EMPATHIC Framework for Task Learning from Implicit Human Feedback](https://mlsanity.com/paper/2009.13649/) (2020)
- [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)
- [Data Leakage and Evaluation Issues in Micro-Expression Analysis](https://mlsanity.com/paper/2211.11425/) (2022)
- [Geometric Graph Representation with Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition](https://mlsanity.com/paper/2205.00380/) (2022)
- [How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?](https://mlsanity.com/paper/2112.01730/) (2021)
- [RMES: Real-Time Micro-Expression Spotting Using Phase From Riesz Pyramid](https://mlsanity.com/paper/2305.05523/) (2023)
- [Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition](https://mlsanity.com/paper/2203.05208/) (2022)
- [Hierarchical Space-Time Attention for Micro-Expression Recognition](https://mlsanity.com/paper/2405.03202/) (2024)
- [Region attention and graph embedding network for occlusion objective class-based micro-expression recognition](https://mlsanity.com/paper/2107.05904/) (2021)
- [A Dynamic 3D Spontaneous Micro-expression Database: Establishment and Evaluation](https://mlsanity.com/paper/2108.00166/) (2021)
- [AHMSA-Net: Adaptive Hierarchical Multi-Scale Attention Network for Micro-Expression Recognition](https://mlsanity.com/paper/2501.02539/) (2025)
- [Micro-expression Recognition Based on Dual-branch Feature Extraction and Fusion](https://mlsanity.com/paper/2602.23950/) (2026)
- [MagPlus: Bridging Micro-to-Regular Facial Expressions through Learnable Magnification](https://mlsanity.com/paper/2606.13312/) (2026)
- [CDER-SME: A Cross-Device Event-RGB Micro-Expression Dataset under Multi-Level Stress Induction](https://mlsanity.com/paper/2606.20715/) (2026)

## Abstract (arXiv)

Micro-expressions are brief spontaneous facial expressions that appear on a face when a person conceals an emotion, making them different to normal facial expressions in subtlety and duration. Currently, emotion classes within the CASME II dataset are based on Action Units and self-reports, creating conflicts during machine learning training. We will show that classifying expressions using Action Units, instead of predicted emotion, removes the potential bias of human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D feature descriptors. The experiments are evaluated on two benchmark FACS coded datasets: CASME II and SAMM. The best result achieves 86.35% accuracy when classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the result of the state-of-the-art 5-class emotional-based classification in CASME II. Results indicate that classification based on Action Units provides an objective method to improve micro-expression recognition.