Facial Image-Based Automatic Assessment of Equine Pain

Recognition of pain in animals is essential for their welfare. However, since there is no verbal communication, this assessment depends solely on the ability of the observer to locate visible or audible signs of pain. The use of grimace scales is proven to be efficient in detecting the pain visually, but the assessment quality depends on the level of training of the assessor and the validity is not easily ensured. There is a clear need for automating the pain assessment process. This work provides a system for pain prediction in horses, based on grimace scales. The pipeline automatically determines the quantitative pose of the equine head and finds facial landmarks before classification, proposing a novel scale-normalisation approach for equine heads. The pain estimation is achieved for each facial region of interest separately, following the clinical pain estimation procedure. We introduce a database of horse images, annotated by professional veterinarians for training and assessment. We also propose a data augmentation method to alleviate the data scarcity issues, which relies on generating realistic 3D equine face models based on 2D annotated images. We show that the data augmentation method improves the performance of both quantitative pose estimation and landmark detection. Our results establish a strong baseline for automatic equine pain estimation.