ArticleSensors (Basel, Switzerland)2024
Unsupervised Canine Emotion Recognition Using Momentum Contrast.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- MViT: A vision transformer with fractal path reordering and dynamic positional encoding.PloS one · 2026Article
- Generative AI Meets Animal Welfare: Evaluating GPT-4 for Pet Emotion Detection.Animals : an open access journal from MDPI · 2025Article
- Identifying Novel Emotions and Wellbeing of Horses from Videos Through Unsupervised Learning.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
4 authors.
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Abstract
We describe a system for identifying dog emotions based on dogs' facial expressions and body posture. Towards that goal, we built a dataset with 2184 images of ten popular dog breeds, grouped into seven similarly sized primal mammalian emotion categories defined by neuroscientist and psychobiologist Jaak Panksepp as 'Exploring', 'Sadness', 'Playing', 'Rage', 'Fear', 'Affectionate' and 'Lust'. We modified the contrastive learning framework MoCo (Momentum Contrast for Unsupervised Visual Representation Learning) to train it on our original dataset and achieved an accuracy of 43.2% and a baseline of 14%. We also trained this model on a second publicly available dataset that resulted in an accuracy of 48.46% but had a baseline of 25%. We compared our unsupervised approach with a supervised model based on a ResNet50 architecture. This model, when tested on our dataset with the seven Panksepp labels, resulted in an accuracy of 74.32.
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