Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System

Automatic face detection is mainly the first step for most of the face-based biometric systems today such as face recognition, facial expression recognition, and tracking head pose. However, face detection technology has various drawbacks caused by challenges in indoor and outdoor environment suc...

Full description

Saved in:

Bibliographic Details
Main Author:	Zakaria, Zulhadi
Format:	Thesis
Language:	English
Published:	2017
Subjects:	T Technology
Online Access:	http://eprints.usm.my/45941/1/Development%20Of%20Hierarchical%20Skin-Adaboost-Neural%20Network%20%28H-Skann%29%20For%20Multiface%20%20Detection%20In%20Video%20Surveillance%20System.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-usm-ep.45941
record_format	uketd_dc
spelling	my-usm-ep.459412021-11-17T03:42:16Z Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System 2017-04-01 Zakaria, Zulhadi T Technology Automatic face detection is mainly the first step for most of the face-based biometric systems today such as face recognition, facial expression recognition, and tracking head pose. However, face detection technology has various drawbacks caused by challenges in indoor and outdoor environment such as uncontrolled lighting and illumination, features occlusions and pose variation. This thesis proposed a technique to detect multiface in video surveillance application with strategic architecture algorithm based on the hierarchical and structural design. This technique consists of two major blocks which are known as Face Skin Localization (FSL) and Hierarchical Skin Area (HSA). FSL is formulated to extract valuable skin data to be processed at the first stage of system detection, which also includes Face Skin Merging (FSM) in order to correctly merge separated skin areas. HSA is proposed to extend the searching of face candidates in selected segmentation area based on the hierarchical architecture strategy, in which each level of the hierarchy employs an integration of Adaboost and Neural Network Algorithm. Experiments were conducted on eleven types database which consists of various challenges to human face detection system. Results reveal that the proposed H-SKANN achieves 98.03% and 97.02% of of averaged accuracy for benchmark database and surveillance area databases, respectively. 2017-04 Thesis http://eprints.usm.my/45941/ http://eprints.usm.my/45941/1/Development%20Of%20Hierarchical%20Skin-Adaboost-Neural%20Network%20%28H-Skann%29%20For%20Multiface%20%20Detection%20In%20Video%20Surveillance%20System.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology
spellingShingle	T Technology Zakaria, Zulhadi Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System
description	Automatic face detection is mainly the first step for most of the face-based biometric systems today such as face recognition, facial expression recognition, and tracking head pose. However, face detection technology has various drawbacks caused by challenges in indoor and outdoor environment such as uncontrolled lighting and illumination, features occlusions and pose variation. This thesis proposed a technique to detect multiface in video surveillance application with strategic architecture algorithm based on the hierarchical and structural design. This technique consists of two major blocks which are known as Face Skin Localization (FSL) and Hierarchical Skin Area (HSA). FSL is formulated to extract valuable skin data to be processed at the first stage of system detection, which also includes Face Skin Merging (FSM) in order to correctly merge separated skin areas. HSA is proposed to extend the searching of face candidates in selected segmentation area based on the hierarchical architecture strategy, in which each level of the hierarchy employs an integration of Adaboost and Neural Network Algorithm. Experiments were conducted on eleven types database which consists of various challenges to human face detection system. Results reveal that the proposed H-SKANN achieves 98.03% and 97.02% of of averaged accuracy for benchmark database and surveillance area databases, respectively.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zakaria, Zulhadi
author_facet	Zakaria, Zulhadi
author_sort	Zakaria, Zulhadi
title	Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System
title_short	Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System
title_full	Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System
title_fullStr	Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System
title_full_unstemmed	Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System
title_sort	development of hierarchical skin-adaboost-neural network (h-skann) for multiface detection in video surveillance system
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2017
url	http://eprints.usm.my/45941/1/Development%20Of%20Hierarchical%20Skin-Adaboost-Neural%20Network%20%28H-Skann%29%20For%20Multiface%20%20Detection%20In%20Video%20Surveillance%20System.pdf
_version_	1747821589907374080

Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System

Similar Items