{"ID":3084695,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-06T20:54:36.964885582Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05458","arxiv_id":"2606.05458","title":"Horse Eye Blink Detection and Classification for Equine Affective State Assessment","abstract":"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.","short_abstract":"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 nake...","url_abs":"https://arxiv.org/abs/2606.05458","url_pdf":"https://arxiv.org/pdf/2606.05458v1","authors":"[\"João Alves\",\"Signe Møller-Skuldbøl\",\"Pia Haubro Andersen\",\"Rikke Gade\"]","published":"2026-06-03T21:33:39Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}