MoodSync: Leveraging Facial Expressions for Personalized Music and Movie Recommendations

P. Rajapandian, M. Shakila, K. Samuvel

Abstract: The present-day cultivation of personalized recommendations stands as a major stone for user engagement in entertainment platforms. However, traditional recommendation systems, presuming that consuming behavior or explicit user preferences best define intentions, have been found wanting in capturing extant emotional contexts for everybody. At this juncture, MoodSync aims to fill that abyss, garnering facial expression analysis methods for dynamically matching music and movie recommendations to the mood of the users. MoodSync, thus, imbibes in itself Monica facial recognition and micro-expression analysis for mood evaluation by studying subtle expressions like micro-expressions, gaze direction, and minimal muscular movements. Interpretation of these may constitute mapping subtle cues to emotional categories such as happiness, sadness, anger, surprise, or calm. This map enables the system to profile a user in real-time mood detection. This emotional information is then fed into a carefully curated media database wherein music tracks and movies are tagged not only with respect to genre and popularity but more importantly on emotional dimensions. Consideration of contrary emotional information forms a reactive engine, which acts contrary to the immediate emotional state of the user, thereby basing content suggestions more relevantly on the delight the user can derive from actual experiences. The system comprises a front end for facial capture in real-time; the back end contains the machine learning engine that performs emotion classification and the recommendation module, which is powered by sentiment-tagged metadata. In terms of confidentiality and respect for user consent, MoodSync is designed in such a way that facial data are treated in an environmentally secure manner, with the least storage possible. Initial testing shows that content programs that are aware of moods greatly increase user satisfaction and engagement. Placing emotion intelligence into content delivery, therefore, recharacterizes personalization, making it more about feeling than about taste. Future prospects might include voice tone analysis and the use of contextual cues within which to act, further enhancing its empathetic interaction model. MoodSync is an advancement in affective computing and offers a platform for emotionally responsive digital environments that understand and adapt to human emotions.

Keywords: Emotion-aware recommendation system, Facial expression recognition, Real-time emotion detection, Emotion-driven AI, deep learning, CNN