Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani

Over a few decades, many computer vision systems haven been developed. One of the applications related to computer vision is face recognition and was being interested by many researches. This project is all about implementing the back-propagation neural network algorithm in classification of face ex...

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Main Author:	Ghani, Mazuraini
Format:	Thesis
Language:	English
Published:	2005
Subjects:	Neural networks (Computer science) Pattern recognition systems
Online Access:	http://ir.uitm.edu.my/id/eprint/9393/ http://ir.uitm.edu.my/id/eprint/9393/1/TD_MAZURAINI%20GHANI%20CS%2005_24.pdf

id	uitm-9393
recordtype	eprints
spelling	uitm-93932017-01-25T07:09:37Z http://ir.uitm.edu.my/id/eprint/9393/ Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani Ghani, Mazuraini Neural networks (Computer science) Pattern recognition systems Over a few decades, many computer vision systems haven been developed. One of the applications related to computer vision is face recognition and was being interested by many researches. This project is all about implementing the back-propagation neural network algorithm in classification of face expression. This project has 3 objectives. The first objective is to collect and digitized images with different expressions which is neutral, happy and angry. The second is to design and develop a prototype for classifying human emotions by face expression recognition of a given image, using back propagation neural network. The last is to study and experiment the suitable edge detection techniques for binary facial image. There are two important phases that were focused in developing this project. The phases are pre-processmg phase and neural network design phase. In preprocessing phase, s detail studies and intensive experiments were conducted to obtain a suitable method of segmentation. Meanwhile, in the neural network design and implementation phase, intensive experiments have been conducted to obtained appropriate design and parameter value of neural network. In this project, the suitable method of segmentation is local adaptive threshold. However, the performances of neural network in learning and classification task should be enhanced by redesigning and conducting experiment on other learning algorithm than back-propagation. 2005 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/9393/1/TD_MAZURAINI%20GHANI%20CS%2005_24.pdf Ghani, Mazuraini (2005) Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani. Degree thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Neural networks (Computer science) Pattern recognition systems
spellingShingle	Neural networks (Computer science) Pattern recognition systems Ghani, Mazuraini Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani
description	Over a few decades, many computer vision systems haven been developed. One of the applications related to computer vision is face recognition and was being interested by many researches. This project is all about implementing the back-propagation neural network algorithm in classification of face expression. This project has 3 objectives. The first objective is to collect and digitized images with different expressions which is neutral, happy and angry. The second is to design and develop a prototype for classifying human emotions by face expression recognition of a given image, using back propagation neural network. The last is to study and experiment the suitable edge detection techniques for binary facial image. There are two important phases that were focused in developing this project. The phases are pre-processmg phase and neural network design phase. In preprocessing phase, s detail studies and intensive experiments were conducted to obtain a suitable method of segmentation. Meanwhile, in the neural network design and implementation phase, intensive experiments have been conducted to obtained appropriate design and parameter value of neural network. In this project, the suitable method of segmentation is local adaptive threshold. However, the performances of neural network in learning and classification task should be enhanced by redesigning and conducting experiment on other learning algorithm than back-propagation.
format	Thesis
author	Ghani, Mazuraini
author_facet	Ghani, Mazuraini
author_sort	Ghani, Mazuraini
title	Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani
title_short	Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani
title_full	Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani
title_fullStr	Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani
title_full_unstemmed	Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani
title_sort	face expression recognition using artificial neural network (ann) / mazuraini ghani
publishDate	2005
url	http://ir.uitm.edu.my/id/eprint/9393/ http://ir.uitm.edu.my/id/eprint/9393/1/TD_MAZURAINI%20GHANI%20CS%2005_24.pdf
first_indexed	2023-09-18T22:47:43Z
last_indexed	2023-09-18T22:47:43Z
_version_	1777417336164188160

Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani

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