Style-transferred cat face images, with style sources chosen by landmark accuracy, improve a 48-point cat facial landmark detector when added to the training set.
DogFLW: Dog Facial Landmarks in the Wild Dataset
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abstract
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.
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cs.CV 1years
2025 1verdicts
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Semantic Style Transfer for Enhancing Animal Facial Landmark Detection
Style-transferred cat face images, with style sources chosen by landmark accuracy, improve a 48-point cat facial landmark detector when added to the training set.