Dog facial landmarks detection and its applications for facial analysis

Automated analysis of facial expressions is a crucial challenge in the emerging field of animal affective computing. One of the most promising approaches in this context is facial landmarks, which are well-studied for humans and are now being adopted for many non-human species. The scarcity of high-quality, comprehensive datasets is a significant challenge in the field. This paper is the first to present a novel Dog Facial Landmarks in the Wild (DogFLW) dataset containing 3732 images of dogs annotated with facial landmarks and bounding boxes. Our facial landmark scheme has 46 landmarks grounded in canine facial anatomy, the Dog Facial Action Coding System (DogFACS), and informed by existing cross-species landmarking methods. We additionally provide a benchmark for dog facial landmarks detection and demonstrate two case studies for landmark detection models trained on the DogFLW. The first is a pipeline using landmarks for emotion classification from dog facial expressions from video, and the second is the recognition of DogFACS facial action units (variables), which can enhance the DogFACS coding process by reducing the time needed for manual annotation. The DogFLW dataset aims to advance the field of animal affective computing by facilitating the development of more accurate, interpretable, and scalable tools for analysing facial expressions in dogs with broader potential applications in behavioural science, veterinary practice, and animal-human interaction research.