Deep Learning-Based Dog Expression Recognition

Dog expression recognition has become an important area of research due to its potential applications in various fields such as animal behavior analysis, training, and healthcare. This project aims to develop a deep learning-based system capable of classifying dog expressions into four categories: 'angry', 'happy', 'relaxed', and 'sad'. The system uses advanced object detection algorithms, specifically Yolov9, Yolov11, and Yolov12, to identify and categorize different emotional states in dogs from images. These models are optimized for high performance in recognizing subtle emotional cues, providing a reliable solution for analyzing dog behavior in diverse environments.

The dataset used for training includes labeled images of dogs, where each expression is tagged according to its emotional state. The system's core functionality includes a user-friendly web interface built using HTML, CSS, and JavaScript for the front-end, and Flask for the backend framework, allowing secure and seamless user interaction. The system not only classifies dog Expression but also enables users to upload and analyze images efficiently.

By integrating Yolov9, Yolov11, and Yolov12 models, this project aims to improve the accuracy of expression recognition, addressing challenges such as breed variations, background noise, and inconsistent lighting. The goal is to contribute to tools that enhance understanding of dog behavior, with applications in pet care, training, and veterinary services.

Keywords: Dog Expression Recognition, Deep Learning, Yolov9, Yolov11, Yolov12, Image Classification, Object Detection, Animal Behavior, Flask, Web Interface.