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Surface electromyography-based facial expression recognition in Bi-polar configuration

Problem statement: Facial expression recognition has been improved recently and it has become a significant issue in diagnostic and medical fields, particularly in the areas of assistive technology and rehabilitation. Apart from their usefulness, there are some problems in their applications like pe...

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Main Authors: Shaikh Salleh, Sheikh Hussain, Tan, Tian Swee, Hamedi, Mahyar, Kamarulafizam, Kamarulafizam
Format: Article
Language:English
Published: Science Publications 2011
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/id/eprint/29346/1/MahyarHamedi2011_SurfaceElectromyographyBasedFacial.pdf
http://eprints.utm.my/id/eprint/29346/
http://dx.doi.org/10.3844/jcssp.2011.1407.1415
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spelling my.utm.293462022-01-31T08:41:13Z http://eprints.utm.my/id/eprint/29346/ Surface electromyography-based facial expression recognition in Bi-polar configuration Shaikh Salleh, Sheikh Hussain Tan, Tian Swee Hamedi, Mahyar Kamarulafizam, Kamarulafizam Q Science (General) Problem statement: Facial expression recognition has been improved recently and it has become a significant issue in diagnostic and medical fields, particularly in the areas of assistive technology and rehabilitation. Apart from their usefulness, there are some problems in their applications like peripheral conditions, lightening, contrast and quality of video and images. Approach: Facial Action Coding System (FACS) and some other methods based on images or videos were applied. This study proposed two methods for recognizing 8 different facial expressions such as natural (rest), happiness in three conditions, anger, rage, gesturing 'a' like in apple word and gesturing no by pulling up the eyebrows based on Three-channels in Bi-polar configuration by SEMG. Raw signals were processed in three main steps (filtration, feature extraction and active features selection) sequentially. Processed data was fed into Support Vector Machine and Fuzzy C-Means classifiers for being classified into 8 facial expression groups. Results: 91.8 and 80.4% recognition ratio had been achieved for FCM and SVM respectively. Conclusion: The confirmed enough accuracy and power in this field of study and FCM showed its better ability and performance in comparison with SVM. It's expected that in near future, new approaches in the frequency bandwidth of each facial gesture will provide better results. Science Publications 2011 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/29346/1/MahyarHamedi2011_SurfaceElectromyographyBasedFacial.pdf Shaikh Salleh, Sheikh Hussain and Tan, Tian Swee and Hamedi, Mahyar and Kamarulafizam, Kamarulafizam (2011) Surface electromyography-based facial expression recognition in Bi-polar configuration. Journal of Computer Science, 7 (9). pp. 1407-1415. ISSN 1549-3636 http://dx.doi.org/10.3844/jcssp.2011.1407.1415 DOI:10.3844/jcssp.2011.1407.1415
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Shaikh Salleh, Sheikh Hussain
Tan, Tian Swee
Hamedi, Mahyar
Kamarulafizam, Kamarulafizam
Surface electromyography-based facial expression recognition in Bi-polar configuration
description Problem statement: Facial expression recognition has been improved recently and it has become a significant issue in diagnostic and medical fields, particularly in the areas of assistive technology and rehabilitation. Apart from their usefulness, there are some problems in their applications like peripheral conditions, lightening, contrast and quality of video and images. Approach: Facial Action Coding System (FACS) and some other methods based on images or videos were applied. This study proposed two methods for recognizing 8 different facial expressions such as natural (rest), happiness in three conditions, anger, rage, gesturing 'a' like in apple word and gesturing no by pulling up the eyebrows based on Three-channels in Bi-polar configuration by SEMG. Raw signals were processed in three main steps (filtration, feature extraction and active features selection) sequentially. Processed data was fed into Support Vector Machine and Fuzzy C-Means classifiers for being classified into 8 facial expression groups. Results: 91.8 and 80.4% recognition ratio had been achieved for FCM and SVM respectively. Conclusion: The confirmed enough accuracy and power in this field of study and FCM showed its better ability and performance in comparison with SVM. It's expected that in near future, new approaches in the frequency bandwidth of each facial gesture will provide better results.
format Article
author Shaikh Salleh, Sheikh Hussain
Tan, Tian Swee
Hamedi, Mahyar
Kamarulafizam, Kamarulafizam
author_facet Shaikh Salleh, Sheikh Hussain
Tan, Tian Swee
Hamedi, Mahyar
Kamarulafizam, Kamarulafizam
author_sort Shaikh Salleh, Sheikh Hussain
title Surface electromyography-based facial expression recognition in Bi-polar configuration
title_short Surface electromyography-based facial expression recognition in Bi-polar configuration
title_full Surface electromyography-based facial expression recognition in Bi-polar configuration
title_fullStr Surface electromyography-based facial expression recognition in Bi-polar configuration
title_full_unstemmed Surface electromyography-based facial expression recognition in Bi-polar configuration
title_sort surface electromyography-based facial expression recognition in bi-polar configuration
publisher Science Publications
publishDate 2011
url http://eprints.utm.my/id/eprint/29346/1/MahyarHamedi2011_SurfaceElectromyographyBasedFacial.pdf
http://eprints.utm.my/id/eprint/29346/
http://dx.doi.org/10.3844/jcssp.2011.1407.1415
_version_ 1724073262472757248
score 13.211869

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