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

Zakaria, Zulhadi (2017) Development Of Hierarchical Skin-Adaboost-Neural Network (H-Skann) For Multiface Detection In Video Surveillance System. PhD thesis, Universiti Sains Malaysia.

[img]
Preview
PDF
Download (22MB) | Preview

Abstract

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.

Item Type: Thesis (PhD)
Subjects: T Technology
Divisions: Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Thesis
Depositing User: Mr Mohamed Yunus Mat Yusof
Date Deposited: 20 Jan 2020 08:46
Last Modified: 22 Oct 2020 03:03
URI: http://eprints.usm.my/id/eprint/45941

Actions (login required)

View Item View Item
Share