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Automated facial paralysis detection using deep learning / Nurul Natasha Razlan, Nurbaity Sabri and Raihah Aminuddin

Facial paralysis, stemming from nerve issues, results in the inability to control facial muscles, leading to asymmetry and weakness. This condition not only affects appearance but also disrupts daily activities. Diagnosis is time-consuming and requires specialised expertise and equipment. To address...

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Main Authors: Razlan, Nurul Natasha, Sabri, Nurbaity, Aminuddin, Raihah
Format: Article
Language:English
Published: College of Computing, Informatics, and Mathematics 2024
Subjects:
Integer programming
Online Access:https://ir.uitm.edu.my/id/eprint/106022/1/106022.pdf
https://ir.uitm.edu.my/id/eprint/106022/
https://fskmjebat.uitm.edu.my/pcmj/
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id my.uitm.ir.106022
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spelling my.uitm.ir.1060222025-02-25T08:22:58Z https://ir.uitm.edu.my/id/eprint/106022/ Automated facial paralysis detection using deep learning / Nurul Natasha Razlan, Nurbaity Sabri and Raihah Aminuddin Razlan, Nurul Natasha Sabri, Nurbaity Aminuddin, Raihah Integer programming Facial paralysis, stemming from nerve issues, results in the inability to control facial muscles, leading to asymmetry and weakness. This condition not only affects appearance but also disrupts daily activities. Diagnosis is time-consuming and requires specialised expertise and equipment. To address these challenges, a deep learning-based system is proposed to analyse facial expressions and distinguish between normal and paralyzed states. “Automated Facial Paralysis Detection using Deep Learning” system leveraging the InceptionResNetV2 model, undergoes pre processing, feature extraction, and feature classification. Facial images are pre processed with techniques like data augmentation for robustness. Features are extracted to identify relevant characteristics, which are then classified using InceptionResNetV2. Evaluation on a Kaggle dataset, divided into training, validation, and testing sets with a ratio of 5:1:1, shows an impressive accuracy of 92.7% in identifying normal and paralyzed facial expressions. This underscores InceptionResNetV2's unmatched effectiveness in facial paralysis detection, marking significant progress in healthcare diagnostics. College of Computing, Informatics, and Mathematics 2024-10 Article NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/106022/1/106022.pdf Automated facial paralysis detection using deep learning / Nurul Natasha Razlan, Nurbaity Sabri and Raihah Aminuddin. (2024) Progress in Computer and Mathematics Journal (PCMJ) <https://ir.uitm.edu.my/view/publication/Progress_in_Computer_and_Mathematics_Journal_=28PCMJ=29/>, 1. pp. 504-515. ISSN 3030-6728 (Submitted) https://fskmjebat.uitm.edu.my/pcmj/
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Integer programming
spellingShingle Integer programming
Razlan, Nurul Natasha
Sabri, Nurbaity
Aminuddin, Raihah
Automated facial paralysis detection using deep learning / Nurul Natasha Razlan, Nurbaity Sabri and Raihah Aminuddin
description Facial paralysis, stemming from nerve issues, results in the inability to control facial muscles, leading to asymmetry and weakness. This condition not only affects appearance but also disrupts daily activities. Diagnosis is time-consuming and requires specialised expertise and equipment. To address these challenges, a deep learning-based system is proposed to analyse facial expressions and distinguish between normal and paralyzed states. “Automated Facial Paralysis Detection using Deep Learning” system leveraging the InceptionResNetV2 model, undergoes pre processing, feature extraction, and feature classification. Facial images are pre processed with techniques like data augmentation for robustness. Features are extracted to identify relevant characteristics, which are then classified using InceptionResNetV2. Evaluation on a Kaggle dataset, divided into training, validation, and testing sets with a ratio of 5:1:1, shows an impressive accuracy of 92.7% in identifying normal and paralyzed facial expressions. This underscores InceptionResNetV2's unmatched effectiveness in facial paralysis detection, marking significant progress in healthcare diagnostics.
format Article
author Razlan, Nurul Natasha
Sabri, Nurbaity
Aminuddin, Raihah
author_facet Razlan, Nurul Natasha
Sabri, Nurbaity
Aminuddin, Raihah
author_sort Razlan, Nurul Natasha
title Automated facial paralysis detection using deep learning / Nurul Natasha Razlan, Nurbaity Sabri and Raihah Aminuddin
title_short Automated facial paralysis detection using deep learning / Nurul Natasha Razlan, Nurbaity Sabri and Raihah Aminuddin
title_full Automated facial paralysis detection using deep learning / Nurul Natasha Razlan, Nurbaity Sabri and Raihah Aminuddin
title_fullStr Automated facial paralysis detection using deep learning / Nurul Natasha Razlan, Nurbaity Sabri and Raihah Aminuddin
title_full_unstemmed Automated facial paralysis detection using deep learning / Nurul Natasha Razlan, Nurbaity Sabri and Raihah Aminuddin
title_sort automated facial paralysis detection using deep learning / nurul natasha razlan, nurbaity sabri and raihah aminuddin
publisher College of Computing, Informatics, and Mathematics
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/106022/1/106022.pdf
https://ir.uitm.edu.my/id/eprint/106022/
https://fskmjebat.uitm.edu.my/pcmj/
_version_ 1825165087963348992
score 13.239859

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