Deep Learning-Based Dog Expression Recognition

Modern life is generally stressful, so many people choose to keep pets as a source of emotional support. Dogs are one of the most popular choices for pets. However, dogs cannot speak and cannot express their emotions and state of mind. Therefore, being able to effectively interpret a dog’s emotions can help establish and improve the relationship between humans and pets. This paper proposes a system based on deep learning for automatic recognition of dog expressions. The system aims to quickly identify the emotions of pets, thereby predicting the current emotional state of their dogs at any time, and strengthening the connection between owners and their pets. The study uses a dataset of Border Collies containing five emotions for training. The prediction process first uses the YOLOv8 object detection model to detect parts of the face such as ears, eyes, mouth, and nose in single-frame images containing dog faces, and automatically classifies the emotions of these parts. The confidence-weighted method is then used to calculate the highest-scoring emotion in the single-frame image as the decision result for each target. The experiment combines data augmentation and transfer learning to further improve accuracy and obtain better emotion recognition results. Through the proposed method, the study achieves an accuracy of 90%, which is a 32% improvement compared to other related studies abroad.