Emotion-Infused Music Recommender System Using Inception-ResNet V2 Algorithm

The ever-increasing volume of digital content, and the fusion of multimedia elements has become a promising technology for enhancing user experience in various applications. This research study explores the integration of image-based emotion detection to augment the capabilities of a music recommendation system. It presents a versatile application of the Inception-ResNet-V2 algorithm for real-time emotion detection using a web camera. The algorithm exhibits high accuracy in recognizing a comprehensive range of emotions, including happiness, sadness, anger, fear, disgust, excitement, surprise, and neutrality, employing the FER-2013 dataset. Users will be provided with the option to select their preferred language from Hindi, English, and Tamil. Subsequently, the proposed system delivers personalized music recommendations based on the dynamically detected emotions.