3D face registration across pose variation and facial expression using cross profile alignment

In a 3D face recognition system, face registration is usually employed to compensate the pose variation in a 3D face model. Most previous methods in 3D face registration are based on the well known global-based approach, Iterative Closest Point (ICP). The experiments are usually conducted using clea...

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Main Author:	Anuar, Laili Hayati
Format:	Thesis
Language:	English
Published:	2011
Subjects:	Three-dimensional imaging Computer vision
Online Access:	http://psasir.upm.edu.my/id/eprint/42276/1/FK%202011%2081R.pdf
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id	my-upm-ir.42276
record_format	uketd_dc
spelling	my-upm-ir.422762016-03-14T07:00:51Z 3D face registration across pose variation and facial expression using cross profile alignment 2011-06 Anuar, Laili Hayati In a 3D face recognition system, face registration is usually employed to compensate the pose variation in a 3D face model. Most previous methods in 3D face registration are based on the well known global-based approach, Iterative Closest Point (ICP). The experiments are usually conducted using cleaned and frontalviewed face models, neglecting the facial variation that often occur in real-time scenarios, such as pose variation, facial expression, facial outliers and occlusion. The proposed thesis uses a local-based approach known as Cross Profile Alignment (CPA) as an alternative to the global-based approach, utilizing the facial feature of a face surface as an attempt to cater all the above problems. Among all features on a face surface, nose tip is the most commonly used feature for facial feature landmarking. It is crucial to accurately detect the nose tip as it will affect the overall performance of the registration process. Most of the presented nose tip detection algorithms were developed merely based on the assumption that the nose tip is the highest point on a face, which is not robust enough for face model under large rotation variation and having large facial outliers. Thus, as the first step prior face registration, the thesis proposed a novel nose tip region detection algorithm using localized point signature, developed specially to locate the nose tip region across various facial variation. The experiment conducted on challenging 3D face databases yields good results with 94.77% detection rate for the nose tip region detection algorithm. Based on the nose tip region location, a cross-profile is extracted and face model is compensated for rotation variation and translation displacement. The registration framework with CPA which gained accuracy rate of 93.9% when tested within 10 degrees error margin, outperforms the registration framework with ICP using Average Face Model (AFM) with accuracy rate of 87.7%, with lower processing time. The findings during this work indicate the accuracy and the reliability of the proposed registration framework towards 3D face model with challenging facial variation. Three-dimensional imaging Computer vision 2011-06 Thesis http://psasir.upm.edu.my/id/eprint/42276/ http://psasir.upm.edu.my/id/eprint/42276/1/FK%202011%2081R.pdf application/pdf en public masters Universiti Putra Malaysia Three-dimensional imaging Computer vision
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Three-dimensional imaging Computer vision
spellingShingle	Three-dimensional imaging Computer vision Anuar, Laili Hayati 3D face registration across pose variation and facial expression using cross profile alignment
description	In a 3D face recognition system, face registration is usually employed to compensate the pose variation in a 3D face model. Most previous methods in 3D face registration are based on the well known global-based approach, Iterative Closest Point (ICP). The experiments are usually conducted using cleaned and frontalviewed face models, neglecting the facial variation that often occur in real-time scenarios, such as pose variation, facial expression, facial outliers and occlusion. The proposed thesis uses a local-based approach known as Cross Profile Alignment (CPA) as an alternative to the global-based approach, utilizing the facial feature of a face surface as an attempt to cater all the above problems. Among all features on a face surface, nose tip is the most commonly used feature for facial feature landmarking. It is crucial to accurately detect the nose tip as it will affect the overall performance of the registration process. Most of the presented nose tip detection algorithms were developed merely based on the assumption that the nose tip is the highest point on a face, which is not robust enough for face model under large rotation variation and having large facial outliers. Thus, as the first step prior face registration, the thesis proposed a novel nose tip region detection algorithm using localized point signature, developed specially to locate the nose tip region across various facial variation. The experiment conducted on challenging 3D face databases yields good results with 94.77% detection rate for the nose tip region detection algorithm. Based on the nose tip region location, a cross-profile is extracted and face model is compensated for rotation variation and translation displacement. The registration framework with CPA which gained accuracy rate of 93.9% when tested within 10 degrees error margin, outperforms the registration framework with ICP using Average Face Model (AFM) with accuracy rate of 87.7%, with lower processing time. The findings during this work indicate the accuracy and the reliability of the proposed registration framework towards 3D face model with challenging facial variation.
format	Thesis
qualification_level	Master's degree
author	Anuar, Laili Hayati
author_facet	Anuar, Laili Hayati
author_sort	Anuar, Laili Hayati
title	3D face registration across pose variation and facial expression using cross profile alignment
title_short	3D face registration across pose variation and facial expression using cross profile alignment
title_full	3D face registration across pose variation and facial expression using cross profile alignment
title_fullStr	3D face registration across pose variation and facial expression using cross profile alignment
title_full_unstemmed	3D face registration across pose variation and facial expression using cross profile alignment
title_sort	3d face registration across pose variation and facial expression using cross profile alignment
granting_institution	Universiti Putra Malaysia
publishDate	2011
url	http://psasir.upm.edu.my/id/eprint/42276/1/FK%202011%2081R.pdf
_version_	1747811903533481984

3D face registration across pose variation and facial expression using cross profile alignment

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