# Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing

arXiv: https://arxiv.org/abs/2506.12524 · HTML: https://mlsanity.com/paper/2506.12524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 14 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Event Based, Near Eye Gaze Tracking Beyond 10,000Hz](https://mlsanity.com/paper/2004.03577/) (2020)
- [3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network](https://mlsanity.com/paper/2308.11771/) (2023)
- [Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions](https://mlsanity.com/paper/1901.04656/) (2019)
- [Event-Based Eye Tracking. AIS 2024 Challenge Survey](https://mlsanity.com/paper/2404.11770/) (2024)
- [Micro-expression detection in long videos using optical flow and recurrent neural networks](https://mlsanity.com/paper/1903.10765/) (2019)
- [A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera](https://mlsanity.com/paper/2404.08858/) (2024)
- [Micro-Attention for Micro-Expression recognition](https://mlsanity.com/paper/1811.02360/) (2018)
- [MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye tracking](https://mlsanity.com/paper/2404.12083/) (2024)
- [Co-designing a Sub-millisecond Latency Event-based Eye Tracking System with Submanifold Sparse CNN](https://mlsanity.com/paper/2404.14279/) (2024)
- [Event-Based Eye Tracking. 2025 Event-based Vision Workshop](https://mlsanity.com/paper/2504.18249/) (2025)
- [Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition](https://mlsanity.com/paper/1907.09160/) (2019)
- [EyeTrAES: Fine-grained, Low-Latency Eye Tracking via Adaptive Event Slicing](https://mlsanity.com/paper/2409.18813/) (2024)

## Abstract (arXiv)

Event-based eye tracking holds significant promise for fine-grained cognitive state inference, offering high temporal resolution and robustness to motion artifacts, critical features for decoding subtle mental states such as attention, confusion, or fatigue. In this work, we introduce a model-agnostic, inference-time refinement framework designed to enhance the output of existing event-based gaze estimation models without modifying their architecture or requiring retraining. Our method comprises two key post-processing modules: (i) Motion-Aware Median Filtering, which suppresses blink-induced spikes while preserving natural gaze dynamics, and (ii) Optical Flow-Based Local Refinement, which aligns gaze predictions with cumulative event motion to reduce spatial jitter and temporal discontinuities. To complement traditional spatial accuracy metrics, we propose a novel Jitter Metric that captures the temporal smoothness of predicted gaze trajectories based on velocity regularity and local signal complexity. Together, these contributions significantly improve the consistency of event-based gaze signals, making them better suited for downstream tasks such as micro-expression analysis and mind-state decoding. Our results demonstrate consistent improvements across multiple baseline models on controlled datasets, laying the groundwork for future integration with multimodal affect recognition systems in real-world environments. Our code implementations can be found at https://github.com/eye-tracking-for-physiological-sensing/EyeLoRiN.