Development of background subtraction algorithm for biometric identification

This thesis presents an improved approach for an automatic face detection system. Segmentation of novel or dynamic objects in a scene can be achieved using background subtraction or foreground segmentation. This is a critical early step in most computer vision applications in domains such as surveil...

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Main Author:	Akbah, A. Khalifa
Format:	Thesis
Language:	English
Subjects:	Biometric identification Background Modeling Techniques Automatic face detection system Detection system
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63458/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63458/2/Full%20text.pdf
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spelling	my-unimap-634582019-11-29T07:31:05Z Development of background subtraction algorithm for biometric identification Akbah, A. Khalifa Kenneth Sundaraj, Assoc. Prof. Dr. This thesis presents an improved approach for an automatic face detection system. Segmentation of novel or dynamic objects in a scene can be achieved using background subtraction or foreground segmentation. This is a critical early step in most computer vision applications in domains such as surveillance and human-computer interaction. The proposed system consists of three parts. In the first part, the use of background subtraction algorithm to deal with the problem of lighting changes, shadows and repetitive motions. All previous implementations fail to handle properly one or more common phenomena, such as global illumination changes, shadows, inter-reflections, similarity of foreground color to background and non-static backgrounds (e.g. active video displays or trees waving in the wind). The proposed method is a background model that uses per-pixel, time-adaptive and Gaussian mixtures in the combined input space of pixel neighborhood and luminance invariant color. This combination in itself is novel. In the second part, another technique known as morphological erosion and dilation operators are used to remove the noise in the resulting binary image to improve the accuracy. The third part is accomplished by using a new technique to locate the face position in the image and extract ilfor recognition and identification purposes. The algorithm has been tested in several different lighting conditions and environments. The experimental results show that the method possesses much greater robustness to problematic phenomena than the prior state of the art methods, without sacrificing real-time performance, making it well-suited for a wide range of practical applications in video events which requiring detection in real-time. The experimental results in real time applications show the robustness, reliability and efficiency in fhe proposed approach; they can accurately detect and extract human face 98% of the time, with the ability to detect the face of different types of people gender, skin color and head attire. The proposed algorithm can be executed at 30 to 35 FPS for an image size of 320 x 240 pixel, which is much better when compared with any other real time applications. Universiti Malaysia Perlis (UniMAP) 2008 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/63458 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63458/1/Page%201-24.pdf 0453a0e8e991711974784a0dc237cec6 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63458/2/Full%20text.pdf 73ea89d37a9b13ed9e01f69c085dbf47 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63458/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Biometric identification Background Modeling Techniques Automatic face detection system Detection system School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Kenneth Sundaraj, Assoc. Prof. Dr.
topic	Biometric identification Background Modeling Techniques Automatic face detection system Detection system
spellingShingle	Biometric identification Background Modeling Techniques Automatic face detection system Detection system Akbah, A. Khalifa Development of background subtraction algorithm for biometric identification
description	This thesis presents an improved approach for an automatic face detection system. Segmentation of novel or dynamic objects in a scene can be achieved using background subtraction or foreground segmentation. This is a critical early step in most computer vision applications in domains such as surveillance and human-computer interaction. The proposed system consists of three parts. In the first part, the use of background subtraction algorithm to deal with the problem of lighting changes, shadows and repetitive motions. All previous implementations fail to handle properly one or more common phenomena, such as global illumination changes, shadows, inter-reflections, similarity of foreground color to background and non-static backgrounds (e.g. active video displays or trees waving in the wind). The proposed method is a background model that uses per-pixel, time-adaptive and Gaussian mixtures in the combined input space of pixel neighborhood and luminance invariant color. This combination in itself is novel. In the second part, another technique known as morphological erosion and dilation operators are used to remove the noise in the resulting binary image to improve the accuracy. The third part is accomplished by using a new technique to locate the face position in the image and extract ilfor recognition and identification purposes. The algorithm has been tested in several different lighting conditions and environments. The experimental results show that the method possesses much greater robustness to problematic phenomena than the prior state of the art methods, without sacrificing real-time performance, making it well-suited for a wide range of practical applications in video events which requiring detection in real-time. The experimental results in real time applications show the robustness, reliability and efficiency in fhe proposed approach; they can accurately detect and extract human face 98% of the time, with the ability to detect the face of different types of people gender, skin color and head attire. The proposed algorithm can be executed at 30 to 35 FPS for an image size of 320 x 240 pixel, which is much better when compared with any other real time applications.
format	Thesis
author	Akbah, A. Khalifa
author_facet	Akbah, A. Khalifa
author_sort	Akbah, A. Khalifa
title	Development of background subtraction algorithm for biometric identification
title_short	Development of background subtraction algorithm for biometric identification
title_full	Development of background subtraction algorithm for biometric identification
title_fullStr	Development of background subtraction algorithm for biometric identification
title_full_unstemmed	Development of background subtraction algorithm for biometric identification
title_sort	development of background subtraction algorithm for biometric identification
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63458/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/63458/2/Full%20text.pdf
_version_	1747836856965267456

Development of background subtraction algorithm for biometric identification

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