Abstract
Music is a ubiquitous medium that deeply influences human psychophysiology. Traditional digital music players, however, remain passive tools, relying heavily on manual input for track selection. This active interaction often disrupts the user's immersion and fails to align with instantaneous emotional shifts. This paper proposes an intelligent "AI Mood-Based Music Player" designed to bridge the gap between human affect and digital entertainment. The system leverages a webcam for real-time video capture and employs computer vision (OpenCV) alongside Deep Learning techniques (specifically Convolutional Neural Networks) to classify facial expressions into distinct emotional categories: Happy, Sad, Angry, and Neutral. Upon classification, the system dynamically curates and plays a corresponding playlist. Experimental results demonstrate an average classification accuracy of approximately 85% under controlled lighting conditions, proving the viability of non-intrusive emotion recognition in enhancing user experience.
Keywords
Facial Emotion Recognition (FER), Convolutional Neural Networks (CNN), Human-Computer Interaction (HCI), Affective Computing, Adaptive Multimedia.
Citations
IRE Journals:
Vijaya Aditya N G, Venkatesh S, Yashas Gowda R S, Mohammed Maaz, Abdul Rehman "AI Mood-Based Music Player Using Facial Emotion Recognition" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 146-149 https://doi.org/10.64388/IREV9I6-1712558
IEEE:
Vijaya Aditya N G, Venkatesh S, Yashas Gowda R S, Mohammed Maaz, Abdul Rehman
"AI Mood-Based Music Player Using Facial Emotion Recognition" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712558
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