Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping

Human are commonly good in notifying several emotions via facial expression. In daily human communication, it is crucial for each person to be able to convey his emotions and perceive others respectively using speech, facial expressions and body movements. The computer vision experts are continuousl...

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Main Author:	Izzah Nilamsyukriyah, Buang
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/35187/3/Izzah.pdf
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id	my-unimas-ir.35187
record_format	uketd_dc
spelling	my-unimas-ir.351872023-11-10T02:44:41Z Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping 2021 Izzah Nilamsyukriyah, Buang QA75 Electronic computers. Computer science Human are commonly good in notifying several emotions via facial expression. In daily human communication, it is crucial for each person to be able to convey his emotions and perceive others respectively using speech, facial expressions and body movements. The computer vision experts are continuously learning on how to achieve high performance in analysing faces, especially which occur spontaneously. Malaysian facial database and analysis are still inconspicuous, especially for local ethnicity studies. Hence, this thesis developed MUA Database, the first Malaysian ethnicity facial database, which consists of data from non-actor subjects from 4 local ethnicities that are Chinese, Iban, Indian and Malay. During the data collection, the subjects are encouraged to express facial expressions spontaneously. Facial expressions analyses are done using the database and facial deformation for each ethnicity is evaluated. From the experiments, the performance of HOG, LBP and SIFT are compared for feature extraction, and SVM, Decision Tree and KNN performance are evaluated as classifier. Results show that the combination of HOG features and KNN classifiers are the best pair for ethnic recognition with 96.90% accuracy, whereas HOG features and SVM classifier combination shows the best pair for emotion recognition with 59.10% accuracy. Indian appeared to be the most recognisable among other ethnicities. As for emotion, “happy” appear to be the most conspicuous emotion, whereas “fear” is the least visible among all tested emotion. Universiti Malaysia Sarawak (UNIMAS) 2021 Thesis http://ir.unimas.my/id/eprint/35187/ http://ir.unimas.my/id/eprint/35187/3/Izzah.pdf text en validuser masters University Malaysia Sarawak Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Izzah Nilamsyukriyah, Buang Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping
description	Human are commonly good in notifying several emotions via facial expression. In daily human communication, it is crucial for each person to be able to convey his emotions and perceive others respectively using speech, facial expressions and body movements. The computer vision experts are continuously learning on how to achieve high performance in analysing faces, especially which occur spontaneously. Malaysian facial database and analysis are still inconspicuous, especially for local ethnicity studies. Hence, this thesis developed MUA Database, the first Malaysian ethnicity facial database, which consists of data from non-actor subjects from 4 local ethnicities that are Chinese, Iban, Indian and Malay. During the data collection, the subjects are encouraged to express facial expressions spontaneously. Facial expressions analyses are done using the database and facial deformation for each ethnicity is evaluated. From the experiments, the performance of HOG, LBP and SIFT are compared for feature extraction, and SVM, Decision Tree and KNN performance are evaluated as classifier. Results show that the combination of HOG features and KNN classifiers are the best pair for ethnic recognition with 96.90% accuracy, whereas HOG features and SVM classifier combination shows the best pair for emotion recognition with 59.10% accuracy. Indian appeared to be the most recognisable among other ethnicities. As for emotion, “happy” appear to be the most conspicuous emotion, whereas “fear” is the least visible among all tested emotion.
format	Thesis
qualification_level	Master's degree
author	Izzah Nilamsyukriyah, Buang
author_facet	Izzah Nilamsyukriyah, Buang
author_sort	Izzah Nilamsyukriyah, Buang
title	Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping
title_short	Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping
title_full	Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping
title_fullStr	Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping
title_full_unstemmed	Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping
title_sort	malaysia ethnicity-based facial expression classification and emotion mapping
granting_institution	University Malaysia Sarawak
granting_department	Faculty of Computer Science and Information Technology
publishDate	2021
url	http://ir.unimas.my/id/eprint/35187/3/Izzah.pdf
_version_	1783728451275980800

Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping

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