What question did this study set out to answer?

The aim is to develop a system that accurately recognizes dog emotions from facial images using deep learning.

April 23, 20260 citationsOpen Access

Dog Emotion Recognition Using Deep Learning with an Expandable Image Dataset

Key Points

The aim is to develop a system that accurately recognizes dog emotions from facial images using deep learning.
Implemented a deep learning model with MobileNetV2 and LSTM to analyze image features.
Introduced an expandable dataset structure for continuous enhancement of the model.
Deployed the system as a real-time web application.
Achieved 72.5% accuracy in recognizing emotions.
Outperformed previous methods, including CNN and CNN+RNN, in identifying Angry, Happy, and Sad emotions.
Demonstrated capability for further performance improvements through dataset expansion.

Abstract

This work presents a deep learning-based system for recognizing canine emotions using facial images. The proposed model integrates a MobileNetV2 backbone with an LSTM module to capture spatial dependencies in image features. An expandable dataset structure is introduced, allowing continuous improvement without code modification. The system achieves 72.5% test accuracy and is deployed as a real-time Streamlit web application. The approach demonstrates improved performance over CNN and CNN+RNN baselines for three emotion classes: Angry, Happy, and Sad.

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