Face recognition in different colour spaces

With the development of information technology in today’s globalized world, a great deal of attention has been paid to biometric identification especially the human face recognition. In human face recognition, there is no need to have contact with target which makes it much more convenient as compar...

Full description

Saved in:
Bibliographic Details
Main Author: Noraidil Sufyan Kamsani
Other Authors: Jiang Xudong
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/72044
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-72044
record_format dspace
spelling sg-ntu-dr.10356-720442023-07-07T16:48:55Z Face recognition in different colour spaces Noraidil Sufyan Kamsani Jiang Xudong School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering With the development of information technology in today’s globalized world, a great deal of attention has been paid to biometric identification especially the human face recognition. In human face recognition, there is no need to have contact with target which makes it much more convenient as compared to other methods. There are many applications to this method such as identification of criminals, identification of bank account and human-computer interaction systems. In human face recognition, there must be an input image in which the system will compare it with the ones in the database and identify which person the image belongs to. In some cases, an image may not be recognized at all and it will be regarded as unknown. Input images of known or unknown faces may come in different colour spaces, such as gray-level (black and white), RGB, YCbCr and CMYK. This paper presents the usage of method called Principle Component Analysis to recognize faces of various colour spaces and compare its performance. Bachelor of Engineering 2017-05-24T01:43:42Z 2017-05-24T01:43:42Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/72044 en Nanyang Technological University 49 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Noraidil Sufyan Kamsani
Face recognition in different colour spaces
description With the development of information technology in today’s globalized world, a great deal of attention has been paid to biometric identification especially the human face recognition. In human face recognition, there is no need to have contact with target which makes it much more convenient as compared to other methods. There are many applications to this method such as identification of criminals, identification of bank account and human-computer interaction systems. In human face recognition, there must be an input image in which the system will compare it with the ones in the database and identify which person the image belongs to. In some cases, an image may not be recognized at all and it will be regarded as unknown. Input images of known or unknown faces may come in different colour spaces, such as gray-level (black and white), RGB, YCbCr and CMYK. This paper presents the usage of method called Principle Component Analysis to recognize faces of various colour spaces and compare its performance.
author2 Jiang Xudong
author_facet Jiang Xudong
Noraidil Sufyan Kamsani
format Final Year Project
author Noraidil Sufyan Kamsani
author_sort Noraidil Sufyan Kamsani
title Face recognition in different colour spaces
title_short Face recognition in different colour spaces
title_full Face recognition in different colour spaces
title_fullStr Face recognition in different colour spaces
title_full_unstemmed Face recognition in different colour spaces
title_sort face recognition in different colour spaces
publishDate 2017
url http://hdl.handle.net/10356/72044
_version_ 1772827876325851136