A Deep Learning Framework for Mood-Based Music Recommendation via Facial Expression Analysis

Computer

Music profoundly influences human emotions, motivation, and overall psychological well-being. Numerous studies have demonstrated that musical stimuli can evoke positive neurological and emotional responses. In today’s digital environment, listeners increasingly seek music that resonates with their current mood and emotional state. This paper introduces an intelligent system that leverages computer vision and deep learning to recommend music automatically based on facial emotion analysis. The proposed model detects and interprets a user’s facial expressions to identify their mood, thereby generating personalized song recommendations without the need for manual selection. By integrating algorithms such as Haar Cascade for face detection and Convolutional Neural Networks (CNN) for emotion classification, the system ensures accurate and real-time mood recognition. Additionally, the utilization of an inbuilt camera enhances design efficiency and cost-effectiveness compared to external sensor-based approaches. This study provides a detailed analysis of the design, implementation, and effectiveness of a CNN-driven mood-based music recommendation framework, emphasizing how facial expression recognition can significantly enrich user experience and interaction with music systems.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.