DogFLW: Dog Facial Landmarks in the Wild Dataset
This dataset addresses the problem of limited resources for affective computing in animals, specifically for dogs, but is incremental as it builds on a similar cat dataset.
The authors tackled the shortage of datasets for automated analysis of animal facial expressions by creating DogFLW, a dataset with 3,274 annotated images of dogs using 46 facial anatomy-based landmarks, analogous to an existing cat dataset.
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.