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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Bibliographic Details
Main Author:	Ghani, Mazuraini
Format:	Thesis
Language:	English
Published:	2005
Subjects:	Neural networks (Computer science) Pattern recognition systems
Online Access:	https://ir.uitm.edu.my/id/eprint/9393/1/TD_MAZURAINI%20GHANI%20CS%2005_24.pdf
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id	my-uitm-ir.9393
record_format	uketd_dc
spelling	my-uitm-ir.93932017-01-25T07:09:37Z Face expression recognition using artificial neural network (ANN) / Mazuraini Ghani 2005 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 https://ir.uitm.edu.my/id/eprint/9393/ https://ir.uitm.edu.my/id/eprint/9393/1/TD_MAZURAINI%20GHANI%20CS%2005_24.pdf text en public other degree Universiti Teknologi MARA Faculty of Computer and Mathematical Science
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
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
qualification_name	other
qualification_level	Bachelor degree
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
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Computer and Mathematical Science
publishDate	2005
url	https://ir.uitm.edu.my/id/eprint/9393/1/TD_MAZURAINI%20GHANI%20CS%2005_24.pdf
_version_	1783733155782459392

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

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