Understanding canine emotions often relies on manual behavioral coding, a time-intensive process that is difficult to scale. To address this bottleneck, this study introduces a computer vision approach using 46 anatomy-based facial landmarks to automate the analysis of dog expressions. Researchers developed a dataset of over 3,700 annotated images and trained machine learning models to track subtle facial changes in video data. The findings demonstrate that this automated system successfully classifies distinct emotional states, such as positive anticipation and frustration, while accurately identifying rapid facial movements. Furthermore, utilizing this technology as a computer-assisted coding aid yielded a 41% reduction in the time required for human experts to manually annotate behavior.
Supervised Neural Style Transfer as an Augmentation Technique for Facial Landmark Detection
A major challenge in training accurate facial landmark detection models
