# OFF-ApexNet on Micro-expression Recognition System

arXiv: https://arxiv.org/abs/1805.08699 · HTML: https://mlsanity.com/paper/1805.08699/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2018.

## Built on by (most cited first, 30 of 43)

- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Micron-BERT: BERT-based Facial Micro-Expression Recognition](https://mlsanity.com/paper/2304.03195/) (2023)
- [Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition](https://mlsanity.com/paper/2101.04838/) (2021)
- [HTNet for micro-expression recognition](https://mlsanity.com/paper/2307.14637/) (2023)
- [Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition](https://mlsanity.com/paper/2112.05851/) (2021)
- [MMNet: Muscle motion-guided network for micro-expression recognition](https://mlsanity.com/paper/2201.05297/) (2022)
- [DFME: A New Benchmark for Dynamic Facial Micro-expression Recognition](https://mlsanity.com/paper/2301.00985/) (2023)
- [MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution](https://mlsanity.com/paper/2506.14511/) (2025)
- [Data Leakage and Evaluation Issues in Micro-Expression Analysis](https://mlsanity.com/paper/2211.11425/) (2022)
- [Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition](https://mlsanity.com/paper/2310.04664/) (2023)
- [Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey](https://mlsanity.com/paper/2412.17616/) (2024)
- [Self-Supervised Approach for Facial Movement Based Optical Flow](https://mlsanity.com/paper/2105.01256/) (2021)
- [Meta-Auxiliary Learning for Micro-Expression Recognition](https://mlsanity.com/paper/2404.12024/) (2024)
- [Infused Suppression Of Magnification Artefacts For Micro-AU Detection](https://mlsanity.com/paper/2504.09326/) (2025)
- [Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs](https://mlsanity.com/paper/2210.04935/) (2022)
- [Efficient Neural Architecture Search for Emotion Recognition](https://mlsanity.com/paper/2303.13653/) (2023)
- [Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition](https://mlsanity.com/paper/2406.08997/) (2024)
- [FED-PsyAU: Privacy-Preserving Micro-Expression Recognition via Psychological AU Coordination and Dynamic Facial Motion Modeling](https://mlsanity.com/paper/2507.20557/) (2025)
- [Rethinking Key-frame-based Micro-expression Recognition: A Robust and Accurate Framework Against Key-frame Errors](https://mlsanity.com/paper/2508.06640/) (2025)
- [Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition](https://mlsanity.com/paper/2203.05208/) (2022)
- [Multi-scale multi-modal micro-expression recognition algorithm based on transformer](https://mlsanity.com/paper/2301.02969/) (2023)
- [Multimodal Latent Emotion Recognition from Micro-expression and Physiological Signals](https://mlsanity.com/paper/2308.12156/) (2023)
- [Hierarchical Space-Time Attention for Micro-Expression Recognition](https://mlsanity.com/paper/2405.03202/) (2024)
- [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)
- [Apex-Centered Spatio-Temporal Rank Pooling and Gradient Attention for Micro-Expression Recognition](https://mlsanity.com/paper/2509.00056/) (2025)
- [Micro-Expression Recognition via Fine-Grained Dynamic Perception](https://mlsanity.com/paper/2509.06015/) (2025)
- [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)
- [An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition](https://mlsanity.com/paper/2310.10022/) (2023)

## Abstract (arXiv)

When a person attempts to conceal an emotion, the genuine emotion is manifest as a micro-expression. Exploration of automatic facial micro-expression recognition systems is relatively new in the computer vision domain. This is due to the difficulty in implementing optimal feature extraction methods to cope with the subtlety and brief motion characteristics of the expression. Most of the existing approaches extract the subtle facial movements based on hand-crafted features. In this paper, we address the micro-expression recognition task with a convolutional neural network (CNN) architecture, which well integrates the features extracted from each video. A new feature descriptor, Optical Flow Features from Apex frame Network (OFF-ApexNet) is introduced. This feature descriptor combines the optical ow guided context with the CNN. Firstly, we obtain the location of the apex frame from each video sequence as it portrays the highest intensity of facial motion among all frames. Then, the optical ow information are attained from the apex frame and a reference frame (i.e., onset frame). Finally, the optical flow features are fed into a pre-designed CNN model for further feature enhancement as well as to carry out the expression classification. To evaluate the effectiveness of OFF-ApexNet, comprehensive evaluations are conducted on three public spontaneous micro-expression datasets (i.e., SMIC, CASME II and SAMM). The promising recognition result suggests that the proposed method can optimally describe the significant micro-expression details. In particular, we report that, in a multi-database with leave-one-subject-out cross-validation experimental protocol, the recognition performance reaches 74.60% of recognition accuracy and F-measure of 71.04%. We also note that this is the first work that performs cross-dataset validation on three databases in this domain.