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Comparison of face recognition techniques : The effects of features and parameters setting

Face recognition is an important biometric application. In this thesis, a comparison of two common techniques for face recognition is carried out under the same conditions. The first technique is the Principal Component Analysis (PCA) while the second is Linear Discriminant Analysis (LDA). In additi...

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Main Author: Ervin Gubin Moung
Format: Thesis
Language:English
English
Published: 2013
Subjects:
TA1501-1820 Applied optics. Photonics
Online Access:https://eprints.ums.edu.my/id/eprint/41788/1/24%20PAGES.pdf
https://eprints.ums.edu.my/id/eprint/41788/2/FULLTEXT.pdf
https://eprints.ums.edu.my/id/eprint/41788/
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spelling my.ums.eprints.417882024-11-29T04:53:15Z https://eprints.ums.edu.my/id/eprint/41788/ Comparison of face recognition techniques : The effects of features and parameters setting Ervin Gubin Moung TA1501-1820 Applied optics. Photonics Face recognition is an important biometric application. In this thesis, a comparison of two common techniques for face recognition is carried out under the same conditions. The first technique is the Principal Component Analysis (PCA) while the second is Linear Discriminant Analysis (LDA). In addition, the performance of PCA and PCA (with Radon) was also carried out. The Euclidean distance is used as the matching criteria. An investigation of the effect of the parameters of PCA on the performance of the face recognition system is carried out. First, it was found that the number of eigenvalues used affects the recognition rates of the system. The maximum number of eigenvalues used is 300. The equal correct rates increases from 1 until 40 to 80 eigenvalues used then become steady afterwards regardless of the image size used. Second, it was found that the higher the number of training images per person the lower the false acceptance rate. Third, the image size used effect the recognition rate when a fixed number of eigenvalues used. However, different image size has their own their optimum number of eigenvalues to achieve highest equal correct rate. When optimum eigenvalues used, their recognition rate did not vary significantly. A comparison of performance, time and resource used by all face recognition system is presented. Four individual systems are compared; PCA, PCA with Radon, LDA, and LDA with Radon. Each individual system gives recognition rate of 89%, 88%, 94%, and 92% respectively with LDA outperform the other three techniques. It was found that no improvement on recognition rate when PCA and LDA use the Radon Transform features as input showing that applying Radon Transform on properly normalized frontal image does not boost the recognition performance. When compared the individual system to the data fusion system, it was found out that data fusion system gives better recognition rate than all the individual face recognition system. Fusion of PCA, LDA, and LDA with Radon give the best recognition performance, giving 98% correct recall and reject rate, and uses 62.8 second process time and 22.2 MB space. 2013 Thesis NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/41788/1/24%20PAGES.pdf text en https://eprints.ums.edu.my/id/eprint/41788/2/FULLTEXT.pdf Ervin Gubin Moung (2013) Comparison of face recognition techniques : The effects of features and parameters setting. Masters thesis, Universiti Malaysia Sabah.
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic TA1501-1820 Applied optics. Photonics
spellingShingle TA1501-1820 Applied optics. Photonics
Ervin Gubin Moung
Comparison of face recognition techniques : The effects of features and parameters setting
description Face recognition is an important biometric application. In this thesis, a comparison of two common techniques for face recognition is carried out under the same conditions. The first technique is the Principal Component Analysis (PCA) while the second is Linear Discriminant Analysis (LDA). In addition, the performance of PCA and PCA (with Radon) was also carried out. The Euclidean distance is used as the matching criteria. An investigation of the effect of the parameters of PCA on the performance of the face recognition system is carried out. First, it was found that the number of eigenvalues used affects the recognition rates of the system. The maximum number of eigenvalues used is 300. The equal correct rates increases from 1 until 40 to 80 eigenvalues used then become steady afterwards regardless of the image size used. Second, it was found that the higher the number of training images per person the lower the false acceptance rate. Third, the image size used effect the recognition rate when a fixed number of eigenvalues used. However, different image size has their own their optimum number of eigenvalues to achieve highest equal correct rate. When optimum eigenvalues used, their recognition rate did not vary significantly. A comparison of performance, time and resource used by all face recognition system is presented. Four individual systems are compared; PCA, PCA with Radon, LDA, and LDA with Radon. Each individual system gives recognition rate of 89%, 88%, 94%, and 92% respectively with LDA outperform the other three techniques. It was found that no improvement on recognition rate when PCA and LDA use the Radon Transform features as input showing that applying Radon Transform on properly normalized frontal image does not boost the recognition performance. When compared the individual system to the data fusion system, it was found out that data fusion system gives better recognition rate than all the individual face recognition system. Fusion of PCA, LDA, and LDA with Radon give the best recognition performance, giving 98% correct recall and reject rate, and uses 62.8 second process time and 22.2 MB space.
format Thesis
author Ervin Gubin Moung
author_facet Ervin Gubin Moung
author_sort Ervin Gubin Moung
title Comparison of face recognition techniques : The effects of features and parameters setting
title_short Comparison of face recognition techniques : The effects of features and parameters setting
title_full Comparison of face recognition techniques : The effects of features and parameters setting
title_fullStr Comparison of face recognition techniques : The effects of features and parameters setting
title_full_unstemmed Comparison of face recognition techniques : The effects of features and parameters setting
title_sort comparison of face recognition techniques : the effects of features and parameters setting
publishDate 2013
url https://eprints.ums.edu.my/id/eprint/41788/1/24%20PAGES.pdf
https://eprints.ums.edu.my/id/eprint/41788/2/FULLTEXT.pdf
https://eprints.ums.edu.my/id/eprint/41788/
_version_ 1817843844136304640
score 13.239859

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