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Predicting Dog Emotions Based on Posture Analysis Using DeepLabCut

Author(s)
Ferres, Kim; Schloesser, Timo; Gloor, Peter A.
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Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/
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Abstract
This paper describes an emotion recognition system for dogs automatically identifying the emotions anger, fear, happiness, and relaxation. It is based on a previously trained machine learning model, which uses automatic pose estimation to differentiate emotional states of canines. Towards that goal, we have compiled a picture library with full body dog pictures featuring 400 images with 100 samples each for the states “Anger”, “Fear”, “Happiness” and “Relaxation”. A new dog keypoint detection model was built using the framework DeepLabCut for animal keypoint detector training. The newly trained detector learned from a total of 13,809 annotated dog images and possesses the capability to estimate the coordinates of 24 different dog body part keypoints. Our application is able to determine a dog’s emotional state visually with an accuracy between 60% and 70%, exceeding human capability to recognize dog emotions.
Date issued
2022-03-22
URI
https://hdl.handle.net/1721.1/141371
Department
Massachusetts Institute of Technology. Center for Collective Intelligence
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Future Internet 14 (4): 97 (2022)
Version: Final published version

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