Facial Emotion Detection and Music Recommendation using Deep Learning
Ghanshyam Bagadi · International Journal for Research in Applied Science and Engineering Technology · 2025
Customary music recommendation systems depend on past tuning in history and course slants to recommend unused music. In any case, this will lead to clients being proposed music that's comparable to what they have as of presently tuned in. This paper proposes an unused music proposal framework that livelihoods multimodal feeling affirmation to endorse music that's custom-fitted to the user's current personality. The system businesses significant learning illustrates to distinguish the user's sentiments from their facial expressions and other multimodal signals. Once the user's sentiments have been recognized, the system endorses music that's likely to facilitate those sentiments. The proposed system is more exact than single-modal or other procedures that have been utilized in the past. More often than not since the system takes into thought various sources of information nearly the user's sentiments. The makers acknowledge that their exploration has the potential to revolutionize the way that people tune in to music. By endorsing music that's customfitted to the user's current disposition, the system can offer help to clients to discover unused music that they appreciate and to have a more personalized music tuning-in experience.