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Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

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Unsupervised Canine Emotion Recognition Using Momentum Contrast.

Aarya Bhave¹, Alina Hafner², Anushka Bhave¹

¹MIT System Design and Management, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02142, USA.

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

Researchers developed a system to identify dog emotions using facial expressions and body posture. This AI model achieved 74.32% accuracy in classifying canine emotions, offering insights into animal behavior.

Keywords:

Panksepp seven emotions canine emotions contrastive learning momentum contrast unsupervised learning

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Area of Science:

Computer Vision
Animal Behavior
Machine Learning

Background:

Accurate identification of animal emotions is crucial for welfare and human-animal interaction.
Existing methods for canine emotion recognition are limited, necessitating advanced computational approaches.

Purpose of the Study:

To develop and evaluate a system for identifying dog emotions from images.
To categorize canine emotional states based on facial expressions and body posture using machine learning.

Main Methods:

A dataset of 2184 dog images across ten breeds was created, labeled into seven primal mammalian emotion categories.
The Momentum Contrast (MoCo) unsupervised learning framework was adapted for emotion identification.
A supervised ResNet50 model was implemented for comparative analysis.

Main Results:

The adapted MoCo model achieved 43.2% accuracy on the custom dataset and 48.46% on a public dataset.
The supervised ResNet50 model attained 74.32% accuracy on the custom dataset using the defined emotion labels.
Unsupervised learning provided a baseline, while supervised learning demonstrated higher performance in dog emotion classification.

Conclusions:

Supervised learning models, such as ResNet50, significantly outperform unsupervised methods like MoCo for dog emotion recognition.
The study highlights the potential of computer vision for objective assessment of canine emotional states.
Further research can refine models for more nuanced understanding of animal emotions.