Deep Learning Models for Automated Classification of Dog Emotional States from Facial Expressions

Authors:
  • TBTali Boneh-Shitrit
    Tali Boneh-Shitrit
  • SAShir Amir
    Shir Amir
  • ABAnnika Bremhorst
    Annika Bremhorst
  • DMDaniel S. Mills
    Daniel S. Mills
  • DFDror Fried
    Dror Fried
  • AZAnna Zamansky
    Anna Zamansky
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Abstract
Similarly to humans, facial expressions in animals are closely linked with emotional states. However, in contrast to the human domain, automated recognition of emotional states from facial expressions in animals is underexplored, mainly due to difficulties in data collection and establishment of ground truth concerning emotional states of non-verbal users. We apply recent deep learning techniques to classify (positive) anticipation and (negative) frustration of dogs on a dataset collected in a controlled experimental setting. We explore the suitability of different backbones (e.g. ResNet, ViT) under different supervisions to this task, and find that features of a self-supervised pretrained ViT (DINO-ViT) are superior to the other alternatives. To the best of our knowledge, this work is the first to address the task of automatic classification of canine emotions on data acquired in a controlled experiment.
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