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Recognizing facial emotion in real-time using MuWNet a novel deep learning network

Facial expression recognition (FER) is a branch of psychology that studies the classification of human emotions using facial expressions. Particularly, FER can be implemented in a vast array of applications, including education, online entertainment, and even essential fields involving human lives a...

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Main Authors: Mustafa Mohammed Kataa,, Wandeep Kaur,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2024
Online Access:http://journalarticle.ukm.my/23979/1/1%20-%2020.pdf
http://journalarticle.ukm.my/23979/
https://www.ukm.my/apjitm
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spelling my-ukm.journal.239792024-08-12T03:25:28Z http://journalarticle.ukm.my/23979/ Recognizing facial emotion in real-time using MuWNet a novel deep learning network Mustafa Mohammed Kataa, Wandeep Kaur, Facial expression recognition (FER) is a branch of psychology that studies the classification of human emotions using facial expressions. Particularly, FER can be implemented in a vast array of applications, including education, online entertainment, and even essential fields involving human lives and behavior, such as medicine. There are seven universal facial expression categories: surprise, sadness, happiness, contempt, fear, anger, and neutrality. Recognizing all these facial expressions and predicting a person's present mood is a challenging problem for machines. Because of the nature of humans, this challenge presents itself to a computer in a more sophisticated manner. The main objective of this research was to construct a novel deep Convolutional Neural Network (CNN) for facial expression classification that can assist in extracting features from images to identify facial gestures and then apply it in real-time. Various neural network models and classification methods have been introduced in the past to reach cutting-edge accuracy in this industry. Separate studies have investigated the capabilities and effectiveness of CNN models in distinguishing human emotions on the FER2013 dataset. In this study, the proposed MuWNet model has been diversified with several types of layers, such as convolution layers, separable convolution layers, and residual blocks. In addition, applying hyperparameter tweaking to enhance progress. The results of two experiments that have been done on the MuWNet model indicate that the accuracy of the classification in the second experiment was 70.72%, with an increase of 0.14% over the first. Finally, these results appear to be competitive in the field of FER, and it can be stated that the proposed model contributed to the emergence of a classification system for facial expressions. Penerbit Universiti Kebangsaan Malaysia 2024-06-01 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/23979/1/1%20-%2020.pdf Mustafa Mohammed Kataa, and Wandeep Kaur, (2024) Recognizing facial emotion in real-time using MuWNet a novel deep learning network. Asia-Pacific Journal of Information Technology and Multimedia, 13 (1). pp. 1-20. ISSN 2289-2192 https://www.ukm.my/apjitm
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description Facial expression recognition (FER) is a branch of psychology that studies the classification of human emotions using facial expressions. Particularly, FER can be implemented in a vast array of applications, including education, online entertainment, and even essential fields involving human lives and behavior, such as medicine. There are seven universal facial expression categories: surprise, sadness, happiness, contempt, fear, anger, and neutrality. Recognizing all these facial expressions and predicting a person's present mood is a challenging problem for machines. Because of the nature of humans, this challenge presents itself to a computer in a more sophisticated manner. The main objective of this research was to construct a novel deep Convolutional Neural Network (CNN) for facial expression classification that can assist in extracting features from images to identify facial gestures and then apply it in real-time. Various neural network models and classification methods have been introduced in the past to reach cutting-edge accuracy in this industry. Separate studies have investigated the capabilities and effectiveness of CNN models in distinguishing human emotions on the FER2013 dataset. In this study, the proposed MuWNet model has been diversified with several types of layers, such as convolution layers, separable convolution layers, and residual blocks. In addition, applying hyperparameter tweaking to enhance progress. The results of two experiments that have been done on the MuWNet model indicate that the accuracy of the classification in the second experiment was 70.72%, with an increase of 0.14% over the first. Finally, these results appear to be competitive in the field of FER, and it can be stated that the proposed model contributed to the emergence of a classification system for facial expressions.
format Article
author Mustafa Mohammed Kataa,
Wandeep Kaur,
spellingShingle Mustafa Mohammed Kataa,
Wandeep Kaur,
Recognizing facial emotion in real-time using MuWNet a novel deep learning network
author_facet Mustafa Mohammed Kataa,
Wandeep Kaur,
author_sort Mustafa Mohammed Kataa,
title Recognizing facial emotion in real-time using MuWNet a novel deep learning network
title_short Recognizing facial emotion in real-time using MuWNet a novel deep learning network
title_full Recognizing facial emotion in real-time using MuWNet a novel deep learning network
title_fullStr Recognizing facial emotion in real-time using MuWNet a novel deep learning network
title_full_unstemmed Recognizing facial emotion in real-time using MuWNet a novel deep learning network
title_sort recognizing facial emotion in real-time using muwnet a novel deep learning network
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2024
url http://journalarticle.ukm.my/23979/1/1%20-%2020.pdf
http://journalarticle.ukm.my/23979/
https://www.ukm.my/apjitm
_version_ 1809139042213691392
score 13.211869

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