• Sanjana Vijaykumar Phand
• Pranav Kumar Patl
• Dhananjay Vijay Rangat
• Rohan Janardhan Sathe

Music recommendation systems play a pivotal role in enhancing user experience by suggesting personalized music selections. Traditional approaches rely on collaborative and content-based filtering, which may lack accuracy in predicting users' moods. To address this limitation, we propose a novel music recommendation system integrating facial expression analysis. Facial expressions serve as potent indicators of emotional states, thus facilitating more precise music recommendations. Our system employs deep learning models for facial expression recognition and collaborative filtering algorithms for personalized music suggestions. Evaluation using diverse datasets demonstrates the system's efficacy in accurately predicting emotional states and generating personalized music recommendations. Moreover, user studies reflect high satisfaction with the system's ability to provide relevant music suggestions aligned with users' emotional needs. This research underscores the potential of integrating facial expression analysis into music recommendation systems, thereby enriching user experience and engagement.

Convolutional neural networks (CNN),Deep Learning(DL),Emotion Detection, Music Recommender System (MRS),Real-time emotion detection, Viola-Jones Algorithm

"FeelFlow: Music Recommendation System Based on Facial Expression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.o574-o579, April-2024, Available :http://www.jetir.org/papers/JETIR2404F81.pdf

"FeelFlow: Music Recommendation System Based on Facial Expression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppo574-o579, April-2024, Available at : http://www.jetir.org/papers/JETIR2404F81.pdf