A Web Application to Recommend Songs Based on Human Facial Expressions and Emotions

Facial expressions are a common non-verbal way of how humans show and express their emotions to others. Emotions can be categorized as positive and negative emotions, derived from facial expressions, in which negative emotions can affect a person�s behavior and thinking. Music is a common remedy f...

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Main Authors: Hanafi, Q.N.�M., Sulaiman, S., Mahamad, S.
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2024
Online Access:http://scholars.utp.edu.my/id/eprint/38105/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176017068&doi=10.1007%2f978-981-99-7339-2_7&partnerID=40&md5=7473c6c8ee515939483a32b2b381ef4d
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spelling oai:scholars.utp.edu.my:381052023-12-11T03:21:41Z http://scholars.utp.edu.my/id/eprint/38105/ A Web Application to Recommend Songs Based on Human Facial Expressions and Emotions Hanafi, Q.N.â��M. Sulaiman, S. Mahamad, S. Facial expressions are a common non-verbal way of how humans show and express their emotions to others. Emotions can be categorized as positive and negative emotions, derived from facial expressions, in which negative emotions can affect a personâ��s behavior and thinking. Music is a common remedy for people to cope with both positive and negative emotions. The use of deep learning to identify emotions based on human expression can be an effective and efficient way to provide solutions for humans because it can mimic the way humans think while requiring less time and effort. By creating a solution that encompasses recommending songs from emotion detected using deep learning, it can benefit society health and entertainment-wise. This paper presents a project that focuses on developing such a solution and testing its performance and effectiveness to users, in improving their emotions via songs. The method used for this web application is OpenCV and DeepFace as face detector and emotion recognition system, respectively; while the song recommendations are pulled via Spotify API, where all these elements are deployed in a web application using Streamlit. DeepFace has been stated to have an accuracy of around 97 for its facial recognition functionality, along with their facial attribute analysis, which can be considered reliable enough to recognize emotions. For future work, other factors that can help to identify emotions are to be put more focus on, as it is envisaged to improve the emotion recognition system in this web application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Springer Science and Business Media Deutschland GmbH 2024 Article NonPeerReviewed Hanafi, Q.N.â��M. and Sulaiman, S. and Mahamad, S. (2024) A Web Application to Recommend Songs Based on Human Facial Expressions and Emotions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14322. pp. 76-86. ISSN 03029743 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176017068&doi=10.1007%2f978-981-99-7339-2_7&partnerID=40&md5=7473c6c8ee515939483a32b2b381ef4d 10.1007/978-981-99-7339-2₇ 10.1007/978-981-99-7339-2₇
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Facial expressions are a common non-verbal way of how humans show and express their emotions to others. Emotions can be categorized as positive and negative emotions, derived from facial expressions, in which negative emotions can affect a person�s behavior and thinking. Music is a common remedy for people to cope with both positive and negative emotions. The use of deep learning to identify emotions based on human expression can be an effective and efficient way to provide solutions for humans because it can mimic the way humans think while requiring less time and effort. By creating a solution that encompasses recommending songs from emotion detected using deep learning, it can benefit society health and entertainment-wise. This paper presents a project that focuses on developing such a solution and testing its performance and effectiveness to users, in improving their emotions via songs. The method used for this web application is OpenCV and DeepFace as face detector and emotion recognition system, respectively; while the song recommendations are pulled via Spotify API, where all these elements are deployed in a web application using Streamlit. DeepFace has been stated to have an accuracy of around 97 for its facial recognition functionality, along with their facial attribute analysis, which can be considered reliable enough to recognize emotions. For future work, other factors that can help to identify emotions are to be put more focus on, as it is envisaged to improve the emotion recognition system in this web application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
format Article
author Hanafi, Q.N.�M.
Sulaiman, S.
Mahamad, S.
spellingShingle Hanafi, Q.N.�M.
Sulaiman, S.
Mahamad, S.
A Web Application to Recommend Songs Based on Human Facial Expressions and Emotions
author_facet Hanafi, Q.N.�M.
Sulaiman, S.
Mahamad, S.
author_sort Hanafi, Q.N.�M.
title A Web Application to Recommend Songs Based on Human Facial Expressions and Emotions
title_short A Web Application to Recommend Songs Based on Human Facial Expressions and Emotions
title_full A Web Application to Recommend Songs Based on Human Facial Expressions and Emotions
title_fullStr A Web Application to Recommend Songs Based on Human Facial Expressions and Emotions
title_full_unstemmed A Web Application to Recommend Songs Based on Human Facial Expressions and Emotions
title_sort web application to recommend songs based on human facial expressions and emotions
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2024
url http://scholars.utp.edu.my/id/eprint/38105/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176017068&doi=10.1007%2f978-981-99-7339-2_7&partnerID=40&md5=7473c6c8ee515939483a32b2b381ef4d
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