Bhave, A.; Hafner, A.; Bhave, A.; Gloor, P. A. Unsupervised Canine Emotion Recognition using Momentum Contrast. Preprints2024, 2024100927. https://doi.org/10.20944/preprints202410.0927.v1
APA Style
Bhave, A., Hafner, A., Bhave, A., & Gloor, P. A. (2024). Unsupervised Canine Emotion Recognition using Momentum Contrast. Preprints. https://doi.org/10.20944/preprints202410.0927.v1
Chicago/Turabian Style
Bhave, A., Anushka Bhave and Peter A. Gloor. 2024 "Unsupervised Canine Emotion Recognition using Momentum Contrast" Preprints. https://doi.org/10.20944/preprints202410.0927.v1
Abstract
We describe a system for identifying dog emotions based on the 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 that are ‘Exploring’, ‘Sadness’, ‘Playing’, ‘Rage’, ‘Fear’, ‘Affectionate’ and ‘Lust’. We modify the Contrastive Learning framework MoCo (Momentum Contrast for Unsupervised Visual Representation Learning) to train it on our original dataset and achieve an accuracy of 43.2% on 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 having seven Panksepp labels resulted in an accuracy of 74.32%.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
