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...

Full description

Saved in:
Bibliographic Details
Main Author: Izzah Nilamsyukriyah, Buang
Format: Thesis
Language:English
Published: Universiti Malaysia Sarawak (UNIMAS) 2021
Subjects:
Online Access:http://ir.unimas.my/id/eprint/35187/3/Izzah.pdf
http://ir.unimas.my/id/eprint/35187/
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Malaysia Sarawak
Language: English
id my.unimas.ir.35187
record_format eprints
spelling my.unimas.ir.351872023-11-10T02:44:41Z http://ir.unimas.my/id/eprint/35187/ Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping 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 PeerReviewed text en http://ir.unimas.my/id/eprint/35187/3/Izzah.pdf Izzah Nilamsyukriyah, Buang (2021) Malaysia Ethnicity-based Facial Expression Classification and Emotion Mapping. Masters thesis, University Malaysia Sarawak.
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
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
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
publisher Universiti Malaysia Sarawak (UNIMAS)
publishDate 2021
url http://ir.unimas.my/id/eprint/35187/3/Izzah.pdf
http://ir.unimas.my/id/eprint/35187/
_version_ 1783883532718833664