| dc.description.abstract | Recognition of pain in equines is essential for their welfare. However, unlike humans,
equines lack verbal communication and are reliant on external observers to assess their
pain. Observers can assess equine pain using different pain scales, such as the Horse
Grimace Scale, EquiFACS, or EQUUS-ARFAP. Training an observer is time-consuming,
and observers often disagree on a diagnosis. This necessitates the need to automate the
equine pain assessment process. In this work, we provide a system for pain assessment
in equine faces based on the EQUUS-ARFAP scale. The proposed system consists of
four steps, namely, automatic head orientation detection, automatic detection of facial
regions, automatic pain detection for each facial region of interest separately, and automatic
data generation. Our main contributions are a detailed analysis of the usage of Region
of Interest (ROI) as the main representation of the assessment pipeline, instead of facial
landmarks, and the deployment of synthetically generated data. We show improved pain
classification on the publicly available Utrecht University Equine Pain Facial Dataset
dataset and advance the state of the art in this problem. Part of the results produced in this
thesis were published. | |
