Facial expression recognition based on luminance sticker

Facial expressions are the most powerful and natural means of communication among human beings. The face can express emotion sooner than people verbalize or even realize their feelings. By knowing the effectiveness of facial expressions in daily life, recently many researchers have attempted to reco...

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Main Author:	Satiyan, Marimuthu
Format:	Thesis
Language:	English
Subjects:	Facial expression recognition Luminance stickers Facial expression
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33153/1/p%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33153/2/Full%20text.pdf
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id	my-unimap-33153
record_format	uketd_dc
spelling	my-unimap-331532014-03-27T03:11:20Z Facial expression recognition based on luminance sticker Satiyan, Marimuthu Facial expressions are the most powerful and natural means of communication among human beings. The face can express emotion sooner than people verbalize or even realize their feelings. By knowing the effectiveness of facial expressions in daily life, recently many researchers have attempted to recognize the facial expression due to its potential applications in behavioural science, medicine, security, biometrics and human machine interaction. Although many researchers have proposed various methods to detect and recognize facial expressions, this field still remains very challenging especially in real time applications. In this study, luminance stickers based facial expression recognition is proposed. A set of minute luminance stickers are fixed on selected locations in a subject’s face and the expressions are captured by video recording. Face feature variations are identified through the movements of the luminance stickers which represent the facial muscular activities. The recorded videos are processed using some software to extract the 2D coordinate values of each sticker from each frame. This set of 2D coordinates vectors are used as an input data for feature extraction method to recognize facial expressions. In this research, facial expressions are recognized using three feature extraction methods such as AR Model, FFT and DWT with their different orders to study their effectiveness. Six statistical measures such as standard deviation, variance, mean, energy, power and entropy are computed for the extracted feature coefficients of AR Model, FFT and DWT to select the best statistical measure which contributes to higher accuracy of facial expression classification. Conventional validation and cross validation are performed on the selected statistical measures to study the reliability of the classifiers. Three different classifiers namely ANN, kNN and LDA are employed in this study to investigate their performance in correctly classifying a total of eight facial expressions. From this analysis, it is found that the facial expression recognition using DWT gives very promising accuracies ( between 98.83% and 99.15% ) of facial expression classification for kNN and LDA classifiers compared to other feature extraction methods and classifiers. It is, hence, concluded that the proposed method can be used as a valuable tool for facial expression based applications such as human machine interfaces. Universiti Malaysia Perlis (UniMAP) 2012 Thesis en http://dspace.unimap.edu.my:80/dspace/handle/123456789/33153 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33153/1/p%201-24.pdf 1144ac249147a19c69af9744b2bcfd44 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33153/2/Full%20text.pdf cee6a691a9f60ecf719d8c5b6403dcb7 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33153/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Facial expression recognition Luminance stickers Facial expression School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Facial expression recognition Luminance stickers Facial expression
spellingShingle	Facial expression recognition Luminance stickers Facial expression Satiyan, Marimuthu Facial expression recognition based on luminance sticker
description	Facial expressions are the most powerful and natural means of communication among human beings. The face can express emotion sooner than people verbalize or even realize their feelings. By knowing the effectiveness of facial expressions in daily life, recently many researchers have attempted to recognize the facial expression due to its potential applications in behavioural science, medicine, security, biometrics and human machine interaction. Although many researchers have proposed various methods to detect and recognize facial expressions, this field still remains very challenging especially in real time applications. In this study, luminance stickers based facial expression recognition is proposed. A set of minute luminance stickers are fixed on selected locations in a subject’s face and the expressions are captured by video recording. Face feature variations are identified through the movements of the luminance stickers which represent the facial muscular activities. The recorded videos are processed using some software to extract the 2D coordinate values of each sticker from each frame. This set of 2D coordinates vectors are used as an input data for feature extraction method to recognize facial expressions. In this research, facial expressions are recognized using three feature extraction methods such as AR Model, FFT and DWT with their different orders to study their effectiveness. Six statistical measures such as standard deviation, variance, mean, energy, power and entropy are computed for the extracted feature coefficients of AR Model, FFT and DWT to select the best statistical measure which contributes to higher accuracy of facial expression classification. Conventional validation and cross validation are performed on the selected statistical measures to study the reliability of the classifiers. Three different classifiers namely ANN, kNN and LDA are employed in this study to investigate their performance in correctly classifying a total of eight facial expressions. From this analysis, it is found that the facial expression recognition using DWT gives very promising accuracies ( between 98.83% and 99.15% ) of facial expression classification for kNN and LDA classifiers compared to other feature extraction methods and classifiers. It is, hence, concluded that the proposed method can be used as a valuable tool for facial expression based applications such as human machine interfaces.
format	Thesis
author	Satiyan, Marimuthu
author_facet	Satiyan, Marimuthu
author_sort	Satiyan, Marimuthu
title	Facial expression recognition based on luminance sticker
title_short	Facial expression recognition based on luminance sticker
title_full	Facial expression recognition based on luminance sticker
title_fullStr	Facial expression recognition based on luminance sticker
title_full_unstemmed	Facial expression recognition based on luminance sticker
title_sort	facial expression recognition based on luminance sticker
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33153/1/p%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/33153/2/Full%20text.pdf
_version_	1747836805352259584

Facial expression recognition based on luminance sticker

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