  Affective computing for animals is a rapidly expanding research area that is
going deeper than automated movement tracking to address animal internal
states, like pain and emotions. Facial expressions can serve to communicate
information about these states in mammals. However, unlike human-related
studies, there is a significant shortage of datasets that would enable the
automated analysis of animal facial expressions. Inspired by the recently
introduced Cat Facial Landmarks in the Wild dataset, presenting cat faces
annotated with 48 facial anatomy-based landmarks, in this paper, we develop an
analogous dataset containing 3,274 annotated images of dogs. Our dataset is
based on a scheme of 46 facial anatomy-based landmarks. The DogFLW dataset is
available from the corresponding author upon a reasonable request.
