Human face verification under illumination variation

The appearance of a face will vary intensely when the illumination changes. The changes in the illumination conditions during image capturing make it difficult to obtain accurate face verification. Changes in illuminations results in two main problems, which are reflections and shadows. One of the m...

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Main Author:	Emadi, Mehran
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/35853/5/MehranEmadiPFKE2013.pdf
Tags:	Add Tag illumiination 1

id	my-utm-ep.35853
record_format	uketd_dc
spelling	my-utm-ep.358532017-07-18T07:05:07Z Human face verification under illumination variation 2013-05 Emadi, Mehran TK Electrical engineering. Electronics Nuclear engineering The appearance of a face will vary intensely when the illumination changes. The changes in the illumination conditions during image capturing make it difficult to obtain accurate face verification. Changes in illuminations results in two main problems, which are reflections and shadows. One of the most important aspects influencing the verification accuracy is illumination normalization. This thesis explored the use of fusion normalization methods to improve the performance of face verification under illumination variation. It has been shown that a single normalization technique is inadequate to solve the problems of illumination. In this study, several normalization methods such as Discrete Wavelet Transform, Discrete Cosine Transform, and Classified Appearance based Quotient Image were investigated for illumination normalization. A verification process was performed for each normalization technique and the outputs of the process, which were the likeness scores would be fused together to improve the final output. In the verification step, Principal Component Analysis was used to reduce the vector size of image and Linear Discriminant Analysis was used to extract discriminative information. In addition, un-trained fusion methods such as Max-Rule, Min-Rule, and Ave-Rule were used to get a unified decision with a reduced error rate. Besides that, fusion normalization methods were also used to solve all problems caused by illumination. The experiments were done on XM2VTS and Yale database B. The results of this research showed that the efficiency of Ave-Rule technique is better than other methods for XM2VTS, and the best fusion method for Yale database B is Min-Rule. To evaluate the techniques, the results have been compared with the outcomes of the fusion of each pair of the normalization methods as well as the results obtained from using other techniques. The comparison showed that the fusion of the three normalization techniques offered a better performance as compared to the fusion of two illumination normalization methods. Furthermore, the performance of face verification based on the fusion of the normalization methods was better in comparison to a single normalization technique. 2013-05 Thesis http://eprints.utm.my/id/eprint/35853/ http://eprints.utm.my/id/eprint/35853/5/MehranEmadiPFKE2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70021?site_name=Restricted Repository phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Emadi, Mehran Human face verification under illumination variation
description	The appearance of a face will vary intensely when the illumination changes. The changes in the illumination conditions during image capturing make it difficult to obtain accurate face verification. Changes in illuminations results in two main problems, which are reflections and shadows. One of the most important aspects influencing the verification accuracy is illumination normalization. This thesis explored the use of fusion normalization methods to improve the performance of face verification under illumination variation. It has been shown that a single normalization technique is inadequate to solve the problems of illumination. In this study, several normalization methods such as Discrete Wavelet Transform, Discrete Cosine Transform, and Classified Appearance based Quotient Image were investigated for illumination normalization. A verification process was performed for each normalization technique and the outputs of the process, which were the likeness scores would be fused together to improve the final output. In the verification step, Principal Component Analysis was used to reduce the vector size of image and Linear Discriminant Analysis was used to extract discriminative information. In addition, un-trained fusion methods such as Max-Rule, Min-Rule, and Ave-Rule were used to get a unified decision with a reduced error rate. Besides that, fusion normalization methods were also used to solve all problems caused by illumination. The experiments were done on XM2VTS and Yale database B. The results of this research showed that the efficiency of Ave-Rule technique is better than other methods for XM2VTS, and the best fusion method for Yale database B is Min-Rule. To evaluate the techniques, the results have been compared with the outcomes of the fusion of each pair of the normalization methods as well as the results obtained from using other techniques. The comparison showed that the fusion of the three normalization techniques offered a better performance as compared to the fusion of two illumination normalization methods. Furthermore, the performance of face verification based on the fusion of the normalization methods was better in comparison to a single normalization technique.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Emadi, Mehran
author_facet	Emadi, Mehran
author_sort	Emadi, Mehran
title	Human face verification under illumination variation
title_short	Human face verification under illumination variation
title_full	Human face verification under illumination variation
title_fullStr	Human face verification under illumination variation
title_full_unstemmed	Human face verification under illumination variation
title_sort	human face verification under illumination variation
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/35853/5/MehranEmadiPFKE2013.pdf
_version_	1747816371736018944

Human face verification under illumination variation

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