Facial Emotion Based Song Recommendation System

The impact of Song on human emotions and behavior is a well-established field of study, and there is growing interest in developing systems that can recommend music based on a person's current emotional state. This paper proposes a system that uses computer vision techniques to analyze a person's facial expressions and recommend music that matches their emotional state. The system uses an in-built camera to capture the person's facial expressions, and the captured data is processed using a machine learning algorithm that has been trained on a dataset of facial expressions and corresponding emotional states. The algorithm is designed to predict the person's emotional state based on their facial expressions and recommend music that corresponds to that emotional state. The system's accuracy rate in identifying a person's emotional state and recommending music based on their facial expressions is 84.82%, indicating that it is a reliable and effective tool for recommending music. The system is also convenient and easy to use since it does not require separate hardware for capturing facial expressions, as the camera is already integrated into the device. The proposed system has the potential to be used in a wide range of applications, such as be utilized many different applications call for such as personalized Song recommendations for individuals, Song therapy, and even in public spaces such as shopping malls or airports, where music can be used to improve the mood of the people present. Overall, the proposed system represents an innovative approach to personalized music recommendations, which takes into account a person's emotional state, allowing them to discover new music that resonates with their current mood. Integrate physiological signals to improve accuracy and effectiveness in music recommendation.