Automated recognition of emotional states of horses from facial expressions
Marcelo Feighelstein, Claire Riccie-Bonot, Hana Hasan, Hallel Weinberg, Tidhar Rettig, Maya Segal, Tomer Distelfeld, Ilan Shimshoni, Daniel S. Mills, Anna Zamansky
Abstract
Animal affective computing is an emerging new field, which has so far mainly focused on pain, while other emotional states remain uncharted territories, especially in horses. This study is the first to develop AI models to automatically recognize horse emotional states from facial expressions using data collected in a controlled experiment. We explore two types of pipelines: a deep learning one which takes as input video footage, and a machine learning one which takes as input EquiFACS annotations. The former outperforms the latter, with 76% accuracy in separating between four emotional states: baseline, positive anticipation, disappointment and frustration. Anticipation and frustration were difficult to separate, with only 61% accuracy.