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Music Recommendation System Based on Facial Emotion Recognition

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Kreesha Iyer
Neha Grandhi
Bhagyashree Birje
Priyanka Verma
Published in
Computer Vision and Robotics
Publisher
Springer Nature Switzerland

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Abstract

This chapter explores the development of a music recommendation system that leverages facial emotion recognition to deliver personalized music experiences. The system uses computer vision and machine learning to detect seven distinct emotions from facial expressions, which are then mapped to corresponding music moods. The proposed methodology involves a hybrid model that combines clustering-based and content-based filtering techniques to recommend songs that align with the user's detected emotional state. The dataset used for training includes a diverse range of songs from 1920 to 2020, with features such as emotional depth, style, danceability, and more. The system's effectiveness is demonstrated through experimental results, showcasing its ability to accurately predict emotions and provide relevant music recommendations. The chapter also discusses the potential for further improvements, such as extending the dataset and incorporating NLP tools for deeper lyric analysis. Overall, this research presents a proof of concept for integrating computer vision and recommendation systems to create mood-sensitive music experiences.
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previous chapter DL Based Approach for Assessing the Severity of DR from Retinal Fundus Images
next chapter Indian Sign Language Recognition Using CNN-LSTM Architecture for Enhanced Gesture Prediction
Title
Music Recommendation System Based on Facial Emotion Recognition
Authors
Kreesha Iyer
Neha Grandhi
Bhagyashree Birje
Priyanka Verma
Copyright Year
2026
Book
Computer Vision and Robotics

Print ISBN: 978-3-032-06252-9

Electronic ISBN: 978-3-032-06253-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-06253-6_15
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