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All Journal JOIV : International Journal on Informatics Visualization Journal of Applied Data Sciences IJOEM: Indonesian Journal of Elearning and Multimedia International Journal of Advances in Artificial Intelligence and Machine Learning
Zakaria, Mohd Zaki
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

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Music Recommendation Based on Facial Expression Using Deep Learning Kurniawan, -; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Saringat, Zainuri; Firosha, Ardian
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.3794

Abstract

Music's profound impact on human emotions is essential for creating personalized experiences in entertainment and therapeutic settings. This study introduces a cutting-edge music recommendation system that utilizes facial expression analysis to tailor music suggestions according to the user's emotional state. Our approach integrates a haar-cascade classifier for real-time face detection with a Convolutional Neural Network (CNN) that classifies emotions into seven distinct categories: happiness, sadness, anger, fear, disgust, surprise, and neutrality. This emotionally aware system recommends music tracks corresponding to the user's current emotional condition to enhance mood regulation and overall listener satisfaction. The effectiveness of our system was evaluated through rigorous testing, where the CNN model demonstrated a high degree of accuracy. Notably, the model achieved an overall accuracy of 84.44% in recognizing facial expressions. Precision, recall, and F1 scores consistently exceeded 84%, indicating robust performance across diverse emotional states. These results underscore the system's capability to accurately interpret and respond to complex emotional cues through tailored music suggestions. Integrating advanced deep learning techniques for face and emotion recognition enables our recommendation system to adapt dynamically to the user's emotional fluctuations. This responsiveness ensures a highly personalized music listening experience that reflects the user's feelings and potentially enhances their emotional well-being. By bridging the gap between static user profiles and the dynamic nature of human emotions, our system sets a new standard for personalized technology in music recommendation, promising significant improvements in user engagement and satisfaction.
Co-Authors - Kurniawan, - Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Armoogum, Sheeba Azmi, Nurhafifi Binti Bujang, Nurul Shaira Binti Devi Udariansyah Dewi, Deshinta Arrova Dhidhi Pambudi Efrizoni, Lusiana Eka, Eka Puji Agustini Fadly Fadly Fatoni, Fatoni Firosha, Ardian Hamdani Hamdani Hanan, Nur Syuhana binti Abd Henderi . Hisham, Putri Aisha Athira binti Irwansyah Irwansyah Ismail, Abdul Azim Bin Kasem Alqudah, Mashal Kasim, Shahreen Binti Kurniawan, Tri Basuki M Said Hasibuan Misinem, Misinem Muhammad Nasir Muhayeddin, Abdul Muniif Mohd Nazmi, Che Mohd Alif Saringat, Zainuri Shahreen Kasim, Shahreen