Real-Time Emotion Detection using CNN Integrated with Music Recommendation and Selection

In the modern digital era, personalization has become a prerequisite to enhancing how we relate to technology, particularly in the entertainment sector. This project proposes a smart music recommendation and playback system based on facial emotion recognition to provide music that resonates with the emotional state of an individual. The concept is straightforward but potent: by scanning facial expressions in real time, the system selects music corresponding to the user’s mood, providing a more personalized and interactive listening experience. The system operates by detecting the user’s facial expressions using a webcam or camera. It employs pre-trained Convolutional Neural Network (CNN) models, including CNNModel.h5, to identify emotions such as happiness, sadness, anger, surprise, or neutrality. These models have been trained on emotion recognition datasets to deliver accurate and timely results. When the emotion is detected, it’s matched against a particular set of songs designed to suit that mood. The app is developed using the Django framework to enable everything from linking the machine learning model to managing the music recommendation process. Users may listen to the recommended tracks or opt to replace them with their own choice. This project integrates artificial intelligence with web development in a novel manner, providing an enjoyable and interactive product.that makes technology come across as more emotionally intelligent and responsive in nature.