Real-time facial emotion recognition and mood correction with Spotify integration / Adam Zikri Zailani

This project presents the development of a mobile application aimed at enhancing driver well-being through real-time facial emotion recognition and mood correction. The application utilizes deep learning-based emotion recognition, employing the MobileNetV2 convolutional neural network, to identify f...

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Main Author:	Zailani, Adam Zikri
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Information technology Information systems
Online Access:	https://ir.uitm.edu.my/id/eprint/88974/1/88974.pdf
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id	my-uitm-ir.88974
record_format	uketd_dc
spelling	my-uitm-ir.889742024-03-19T07:06:36Z Real-time facial emotion recognition and mood correction with Spotify integration / Adam Zikri Zailani 2023 Zailani, Adam Zikri Information technology. Information systems This project presents the development of a mobile application aimed at enhancing driver well-being through real-time facial emotion recognition and mood correction. The application utilizes deep learning-based emotion recognition, employing the MobileNetV2 convolutional neural network, to identify four primary emotions - sad, happy, angry, and neutral - in drivers. Upon recognizing negative emotional states, such as anger and sadness, the app responds by playing music from Spotify to uplift the driver's mood. The successful implementation of the mobile app showcases its potential to mitigate negative emotions in drivers, providing a novel approach to promote emotional well-being during driving experiences. Accuracy obtained from controlled environment testing using python coding snippets proved promising with over 90% accuracy across all four emotions. However, the paper also acknowledges certain limitations, including the app's limited emotional spectrum, individual variability in emotional expression, and the challenge of distinguishing genuine anger from naturally angry resting faces. Additionally, technical constraints related to CNN architecture and hardware requirements are discussed. 2023 Thesis https://ir.uitm.edu.my/id/eprint/88974/ https://ir.uitm.edu.my/id/eprint/88974/1/88974.pdf text en public degree Universiti Teknologi MARA, Melaka College of Computing, Informatics and Mathematics
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Information technology Information systems
spellingShingle	Information technology Information systems Zailani, Adam Zikri Real-time facial emotion recognition and mood correction with Spotify integration / Adam Zikri Zailani
description	This project presents the development of a mobile application aimed at enhancing driver well-being through real-time facial emotion recognition and mood correction. The application utilizes deep learning-based emotion recognition, employing the MobileNetV2 convolutional neural network, to identify four primary emotions - sad, happy, angry, and neutral - in drivers. Upon recognizing negative emotional states, such as anger and sadness, the app responds by playing music from Spotify to uplift the driver's mood. The successful implementation of the mobile app showcases its potential to mitigate negative emotions in drivers, providing a novel approach to promote emotional well-being during driving experiences. Accuracy obtained from controlled environment testing using python coding snippets proved promising with over 90% accuracy across all four emotions. However, the paper also acknowledges certain limitations, including the app's limited emotional spectrum, individual variability in emotional expression, and the challenge of distinguishing genuine anger from naturally angry resting faces. Additionally, technical constraints related to CNN architecture and hardware requirements are discussed.
format	Thesis
qualification_level	Bachelor degree
author	Zailani, Adam Zikri
author_facet	Zailani, Adam Zikri
author_sort	Zailani, Adam Zikri
title	Real-time facial emotion recognition and mood correction with Spotify integration / Adam Zikri Zailani
title_short	Real-time facial emotion recognition and mood correction with Spotify integration / Adam Zikri Zailani
title_full	Real-time facial emotion recognition and mood correction with Spotify integration / Adam Zikri Zailani
title_fullStr	Real-time facial emotion recognition and mood correction with Spotify integration / Adam Zikri Zailani
title_full_unstemmed	Real-time facial emotion recognition and mood correction with Spotify integration / Adam Zikri Zailani
title_sort	real-time facial emotion recognition and mood correction with spotify integration / adam zikri zailani
granting_institution	Universiti Teknologi MARA, Melaka
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/88974/1/88974.pdf
_version_	1794192181484847104

Real-time facial emotion recognition and mood correction with Spotify integration / Adam Zikri Zailani

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