Abstract
Recognition of pain in equines is essential for their
welfare. There are several tools, such as the Horse Grimace Scale,
EquiFACS and EQUUS-ARFAP, developed for pain assessment
in equines, and there are approaches to automate assessment,
as training observers takes time, and disagreements between
observers are common. In this work, we provide a system
for pain assessment in equine faces based on the EQUUSARFAP scale. The proposed system consists of three steps,
namely, automatic detection of the facial regions, automatic head
orientation detection, and automatic pain detection for each
facial region of interest separately. Our main contribution is
a detailed analysis of the usage of regions of interest as the
main representation of the assessment pipeline, instead of facial
landmarks. We show improved pain classification on the publicly
available UU Equine Pain Face Dataset and advance the state of
the art in this problem.
welfare. There are several tools, such as the Horse Grimace Scale,
EquiFACS and EQUUS-ARFAP, developed for pain assessment
in equines, and there are approaches to automate assessment,
as training observers takes time, and disagreements between
observers are common. In this work, we provide a system
for pain assessment in equine faces based on the EQUUSARFAP scale. The proposed system consists of three steps,
namely, automatic detection of the facial regions, automatic head
orientation detection, and automatic pain detection for each
facial region of interest separately. Our main contribution is
a detailed analysis of the usage of regions of interest as the
main representation of the assessment pipeline, instead of facial
landmarks. We show improved pain classification on the publicly
available UU Equine Pain Face Dataset and advance the state of
the art in this problem.
| Original language | English |
|---|---|
| Title of host publication | 12th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) |
| Publisher | IEEE |
| Pages | 47-54 |
| DOIs | |
| Publication status | Published - 2024 |