Camera Independent Face Recognition Algorithm In Visual Surveillance

Face recognition in visual surveillance has the ability to reduce crime rates in public area due to the suspect’s identity can be automatically identified in real-time using the face images captured by the surveillance camera as circumstantial evidence. Several available image preprocessing techniqu...

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Main Author:	Yew, Chuu Tian
Format:	Thesis
Language:	English
Published:	2015
Subjects:	TK7870 Electronic packaging
Online Access:	http://eprints.usm.my/40970/1/Yew_Chuu_Tian_24_pages.pdf
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id	my-usm-ep.40970
record_format	uketd_dc
spelling	my-usm-ep.409702018-07-10T09:02:28Z Camera Independent Face Recognition Algorithm In Visual Surveillance 2015 Yew, Chuu Tian TK7870 Electronic packaging Face recognition in visual surveillance has the ability to reduce crime rates in public area due to the suspect’s identity can be automatically identified in real-time using the face images captured by the surveillance camera as circumstantial evidence. Several available image preprocessing techniques, classifiers, and approaches had been proposed and tested to mitigate the effect of illumination variation, pose variations, and intensity quality differences due to hardware differences in such system. The face recognition system should be able to integrate seamlessly into the existing system. From the experiments, Histogram Equalization (HE) preprocessed face images scaled to 30�30 had proven to be well suited for pre-processing of surveillance images. The combination of Linear Discriminant Analysis (LDA) and HE preprocessed images managed to achieve an average recognition rate of 81.48% for the single camera training set. The flandmark facial landmark detector is implemented to determine the location of the eyes and new face images are obtained by cropping the HE pre-processed images. The combination of flandmark images at 20�30 with multi-class Support Vector Machine (SVM) is used to form a multimodal classification system with LDA and HE combination. Score level fusion is done to the normalized output scores of both the classifiers with proper weight, w assigned to each score. Finally, the watch list principle will list out several possible subjects according to their respective score ranking rather than deciding on a particular subject based on the maximum score, thus increasing the performance of the proposed system. The experimental results demonstrate the performance of the proposed algorithm on Surveillance Camera Face Database (SCface) database with 97.45% average recognition rate. 2015 Thesis http://eprints.usm.my/40970/ http://eprints.usm.my/40970/1/Yew_Chuu_Tian_24_pages.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK7870 Electronic packaging
spellingShingle	TK7870 Electronic packaging Yew, Chuu Tian Camera Independent Face Recognition Algorithm In Visual Surveillance
description	Face recognition in visual surveillance has the ability to reduce crime rates in public area due to the suspect’s identity can be automatically identified in real-time using the face images captured by the surveillance camera as circumstantial evidence. Several available image preprocessing techniques, classifiers, and approaches had been proposed and tested to mitigate the effect of illumination variation, pose variations, and intensity quality differences due to hardware differences in such system. The face recognition system should be able to integrate seamlessly into the existing system. From the experiments, Histogram Equalization (HE) preprocessed face images scaled to 30�30 had proven to be well suited for pre-processing of surveillance images. The combination of Linear Discriminant Analysis (LDA) and HE preprocessed images managed to achieve an average recognition rate of 81.48% for the single camera training set. The flandmark facial landmark detector is implemented to determine the location of the eyes and new face images are obtained by cropping the HE pre-processed images. The combination of flandmark images at 20�30 with multi-class Support Vector Machine (SVM) is used to form a multimodal classification system with LDA and HE combination. Score level fusion is done to the normalized output scores of both the classifiers with proper weight, w assigned to each score. Finally, the watch list principle will list out several possible subjects according to their respective score ranking rather than deciding on a particular subject based on the maximum score, thus increasing the performance of the proposed system. The experimental results demonstrate the performance of the proposed algorithm on Surveillance Camera Face Database (SCface) database with 97.45% average recognition rate.
format	Thesis
qualification_level	Master's degree
author	Yew, Chuu Tian
author_facet	Yew, Chuu Tian
author_sort	Yew, Chuu Tian
title	Camera Independent Face Recognition Algorithm In Visual Surveillance
title_short	Camera Independent Face Recognition Algorithm In Visual Surveillance
title_full	Camera Independent Face Recognition Algorithm In Visual Surveillance
title_fullStr	Camera Independent Face Recognition Algorithm In Visual Surveillance
title_full_unstemmed	Camera Independent Face Recognition Algorithm In Visual Surveillance
title_sort	camera independent face recognition algorithm in visual surveillance
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
publishDate	2015
url	http://eprints.usm.my/40970/1/Yew_Chuu_Tian_24_pages.pdf
_version_	1747820850768248832

Camera Independent Face Recognition Algorithm In Visual Surveillance

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