Investigation of nonlinear feature extraction techniques for facial emotion recognition

Over the last decades, facial emotion recognition has received a significant interest among researchers in areas of computer vision, pattern recognition and its related field. The increasing applications of facial emotion recognition have shown a sizeable impact in many areas ranging from psychol...

Full description

Saved in:

Bibliographic Details
Format:	Thesis
Language:	English
Subjects:	Facial expression Emotion recognition Pattern recognition systems Human-computer interaction
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77199/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77199/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77199/4/Hasimah%20Ali.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-unimap-77199
record_format	uketd_dc
spelling	my-unimap-771992022-11-25T01:12:04Z Investigation of nonlinear feature extraction techniques for facial emotion recognition Hariharan, Muthusamy, Dr. Over the last decades, facial emotion recognition has received a significant interest among researchers in areas of computer vision, pattern recognition and its related field. The increasing applications of facial emotion recognition have shown a sizeable impact in many areas ranging from psychology to human-computer interaction (HCI). Although facial emotion recognition has achieved a certain level of success, however its performance is far from human perception. Many approaches have been constantly proposed in the literature. In fact, the ability of facial emotion recognition to operate in fully automated with high accuracy remains challenging due to various problems such as intra-class variations, inter-class similarities and subtle changes of facial features. The adhered problem is further hampered as physiognomies of faces with respect to age, ethnicity and gender, thus increase the difficulties of recognizing the facial emotion. In order to resolve this problem, this thesis aims to develop nonlinear features extraction techniques of using Higher Order Spectra (HOS) and Empirical Mode Decomposition (EMD) separately in recognizing the seven facial emotions (anger, disgust, fear, happiness, neutral, sadness and surprise) from static images. A pre-processing step of isolating face region from different background was first employed by means of face detection. The 2-D facial image was then projected into 1-D facial signal by successive projection via Radon transform. Radon transform is translation and rotation invariant, hence preserves the variations in pixel intensities. The facial signal that describes the expression was extracted using HOS and EMD to obtain a set of significant features. In HOS framework, the third order statistic or bispectrum that captures contour (shape) and texture information was applied on facial signal. In this work, a new set of bispectral features was used to characterize the distinctive features of seven classes of emotion. While, in EMD framework, the facial signal was decomposed using EMD to produce a small set of intrinsic mode functions (IMFs) via sifting process. The IMF features which exhibit the unique pattern were used to differentiate the facial emotions. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77199 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77199/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77199/1/Page%201-24.pdf e3aeb8dd1156bc2d84af485490f0cf55 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77199/2/Full%20text.pdf cc0444d54cb9f7318d1719b9eea55c4e http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77199/4/Hasimah%20Ali.pdf ec7c78798dedc1569497c750be24584e Universiti Malaysia Perlis (UniMAP) Facial expression Emotion recognition Pattern recognition systems Human-computer interaction School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Hariharan, Muthusamy, Dr.
topic	Facial expression Emotion recognition Pattern recognition systems Human-computer interaction
spellingShingle	Facial expression Emotion recognition Pattern recognition systems Human-computer interaction Investigation of nonlinear feature extraction techniques for facial emotion recognition
description	Over the last decades, facial emotion recognition has received a significant interest among researchers in areas of computer vision, pattern recognition and its related field. The increasing applications of facial emotion recognition have shown a sizeable impact in many areas ranging from psychology to human-computer interaction (HCI). Although facial emotion recognition has achieved a certain level of success, however its performance is far from human perception. Many approaches have been constantly proposed in the literature. In fact, the ability of facial emotion recognition to operate in fully automated with high accuracy remains challenging due to various problems such as intra-class variations, inter-class similarities and subtle changes of facial features. The adhered problem is further hampered as physiognomies of faces with respect to age, ethnicity and gender, thus increase the difficulties of recognizing the facial emotion. In order to resolve this problem, this thesis aims to develop nonlinear features extraction techniques of using Higher Order Spectra (HOS) and Empirical Mode Decomposition (EMD) separately in recognizing the seven facial emotions (anger, disgust, fear, happiness, neutral, sadness and surprise) from static images. A pre-processing step of isolating face region from different background was first employed by means of face detection. The 2-D facial image was then projected into 1-D facial signal by successive projection via Radon transform. Radon transform is translation and rotation invariant, hence preserves the variations in pixel intensities. The facial signal that describes the expression was extracted using HOS and EMD to obtain a set of significant features. In HOS framework, the third order statistic or bispectrum that captures contour (shape) and texture information was applied on facial signal. In this work, a new set of bispectral features was used to characterize the distinctive features of seven classes of emotion. While, in EMD framework, the facial signal was decomposed using EMD to produce a small set of intrinsic mode functions (IMFs) via sifting process. The IMF features which exhibit the unique pattern were used to differentiate the facial emotions.
format	Thesis
title	Investigation of nonlinear feature extraction techniques for facial emotion recognition
title_short	Investigation of nonlinear feature extraction techniques for facial emotion recognition
title_full	Investigation of nonlinear feature extraction techniques for facial emotion recognition
title_fullStr	Investigation of nonlinear feature extraction techniques for facial emotion recognition
title_full_unstemmed	Investigation of nonlinear feature extraction techniques for facial emotion recognition
title_sort	investigation of nonlinear feature extraction techniques for facial emotion recognition
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77199/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77199/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77199/4/Hasimah%20Ali.pdf
_version_	1776104248795201536

Investigation of nonlinear feature extraction techniques for facial emotion recognition

Similar Items