Integrated face detection approach for far image application

Face detection has been widely explored over the past few decades. Despite the significant progress in detecting human faces in unconstrained and complex images, face detection remains a challenging problem in computer vision, especially for the images captured at a far distance making it difficu...

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Main Author:	Salka, Tanko Daniel
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Human face recognition (Computer science)
Online Access:	http://psasir.upm.edu.my/id/eprint/67088/1/FK%202016%20130%20IR.pdf
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id	my-upm-ir.67088
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spelling	my-upm-ir.670882019-02-19T03:07:10Z Integrated face detection approach for far image application 2016-10 Salka, Tanko Daniel Face detection has been widely explored over the past few decades. Despite the significant progress in detecting human faces in unconstrained and complex images, face detection remains a challenging problem in computer vision, especially for the images captured at a far distance making it difficult to detect face region. Other factors affecting face detection are illumination conditions, pose and ethnicity. Therefore, the need of a robust and efficient face detection algorithm is required to tackle these problems. This thesis presents an Integrated face detection approach for far image application, which solves the problems mentioned. The proposed approach consists of an Illumination compensation method, a Skin segmentation method, a Noise reduction method and Euler method. In the proposed illumination compensation method, the R, G and B components were normalized using Gray World Theory (GWT), a theory that compensates the illumination effect. The skin segmentation method consists of a combination of RGB filter, the newly proposed filter known as a Dynamic chrominance filter and an edge detector. The function of the RGB filter is to reject pixels with the RGB colors that are most probably non-skin, so that the computation in the following stages does not apply to all pixels. In this method, the final decision of a pixel belongs to the class “skin is made by the Dynamic chrominance filter and the edge detector. The noise reduction method in the proposed algorithm consists of a combination of a morphological filter and a rejection method. The last stage of the algorithm is to apply the Euler method, in which its function is to search for the facial features. The features indicate whether the detected skin region is a region that represents face or non-face. Also, an experiment was conducted on the developed database known as Large Variability Surveillance Camera Face (LVSC) database and FEI database, the proposed method produced a detection rate of 98.4% and 100%, respectively. Human face recognition (Computer science) 2016-10 Thesis http://psasir.upm.edu.my/id/eprint/67088/ http://psasir.upm.edu.my/id/eprint/67088/1/FK%202016%20130%20IR.pdf text en public masters Universiti Putra Malaysia Human face recognition (Computer science)
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Human face recognition (Computer science)
spellingShingle	Human face recognition (Computer science) Salka, Tanko Daniel Integrated face detection approach for far image application
description	Face detection has been widely explored over the past few decades. Despite the significant progress in detecting human faces in unconstrained and complex images, face detection remains a challenging problem in computer vision, especially for the images captured at a far distance making it difficult to detect face region. Other factors affecting face detection are illumination conditions, pose and ethnicity. Therefore, the need of a robust and efficient face detection algorithm is required to tackle these problems. This thesis presents an Integrated face detection approach for far image application, which solves the problems mentioned. The proposed approach consists of an Illumination compensation method, a Skin segmentation method, a Noise reduction method and Euler method. In the proposed illumination compensation method, the R, G and B components were normalized using Gray World Theory (GWT), a theory that compensates the illumination effect. The skin segmentation method consists of a combination of RGB filter, the newly proposed filter known as a Dynamic chrominance filter and an edge detector. The function of the RGB filter is to reject pixels with the RGB colors that are most probably non-skin, so that the computation in the following stages does not apply to all pixels. In this method, the final decision of a pixel belongs to the class “skin is made by the Dynamic chrominance filter and the edge detector. The noise reduction method in the proposed algorithm consists of a combination of a morphological filter and a rejection method. The last stage of the algorithm is to apply the Euler method, in which its function is to search for the facial features. The features indicate whether the detected skin region is a region that represents face or non-face. Also, an experiment was conducted on the developed database known as Large Variability Surveillance Camera Face (LVSC) database and FEI database, the proposed method produced a detection rate of 98.4% and 100%, respectively.
format	Thesis
qualification_level	Master's degree
author	Salka, Tanko Daniel
author_facet	Salka, Tanko Daniel
author_sort	Salka, Tanko Daniel
title	Integrated face detection approach for far image application
title_short	Integrated face detection approach for far image application
title_full	Integrated face detection approach for far image application
title_fullStr	Integrated face detection approach for far image application
title_full_unstemmed	Integrated face detection approach for far image application
title_sort	integrated face detection approach for far image application
granting_institution	Universiti Putra Malaysia
publishDate	2016
url	http://psasir.upm.edu.my/id/eprint/67088/1/FK%202016%20130%20IR.pdf
_version_	1747812441996132352

Integrated face detection approach for far image application

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