Facial Expression-Driven Song Suggestion System

Many individuals often struggle to identify the type of music they truly want to listen to based on their current mood. These challenges can lead to significant time wasted in searching for the "perfect" song that aligns with their emotional state. In this era of rapid technological advancements, particularly in the domain of Artificial Intelligence, leveraging AI in song recommendation systems can bridge this gap, creating a seamless fusion of technology and user needs. This paper aims to develop an emotion-based song suggestion system that suggests songs based on the user's emotional state. The process begins with the user capturing a photo of themselves using their laptop's webcam—an action undertaken only with their explicit consent. Using a Convolutional Neural Network (CNN), the system analyzes the image to detect the users current emotional state. Once the emotion is identified, the system matches it to a curated playlist or category of songs that correspond to the detected mood. The selected song is then played automatically, fulfilling the user's musical preferences effortlessly. The proposed system enhances user engagement by dynamically adapting to their emotions, making it a promising approach for next-generation music recommendation platforms.