Research Focus
Development of Smart Mirror System with Real-Time Emotion Recognition
My research focuses on developing an emotion-aware Smart Mirror system that can recognize human emotions in real time using deep learning and intelligent display technology. The main goal of this project is to improve human–machine interaction by enabling intelligent systems to understand and respond to human emotional states.The research will begin with building a facial emotion recognition model using the FER-2013 dataset. After evaluating and optimizing the model to achieve reliable performance, larger datasets such as RAF-DB and AffectNet will be used to further improve accuracy and robustness. The optimized model will then be integrated into a small robotic Smart Mirror system equipped with a camera, where real-time facial images will be captured and analyzed to predict a user’s emotional state.Based on the detected emotion, the system will provide adaptive feedback through visual or verbal responses, creating a more interactive and empathetic user experience. This project combines computer vision, deep learning, and intelligent interface design using tools such as Python, PyTorch/TensorFlow, and OpenCV.The expected outcome is a functional prototype that can serve as a foundation for future emotion-aware systems in healthcare, education, and personal well-being, contributing to the development of more human-centered intelligent environments.