# Horse Eye Blink Detection and Classification for Equine Affective State Assessment

arXiv: https://arxiv.org/abs/2606.05458 · HTML: https://mlsanity.com/paper/2606.05458/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Automated Detection of Equine Facial Action Units](https://mlsanity.com/paper/2102.08983/) (2021)
- [Read My Ears! Horse Ear Movement Detection for Equine Affective State Assessment](https://mlsanity.com/paper/2505.03554/) (2025)

## Abstract (arXiv)

Automated detection of equine facial action units (AUs) is a promising yet under-explored avenue for pain and affective state assessment in horses. Half and full-blink movements are recognised indicators of pain and stress, but as micro-expressions, their subtle, fine-grained nature makes them easily missed by the naked eye and only discernible through frame-by-frame video inspection, making reliable automated detection from video a particularly demanding task. We develop and evaluate three methods for automated blink classification from horse videos: a frame-based YOLOv12 detector, an optical flow magnitude thresholding approach, and a fine-tuned VideoMAE model, tested on a publicly available dataset. We achieve a macro-F1 score of 0.898 when doing blink classification and 0.926 on binary blink detection. Our results highlight both the potential and the inherent challenges of fine-grained AU detection for equine welfare monitoring.