Spherical-Based Iterative Closest Point With Nose Tip Localization For Unconstraint Viewpoint 3D Face Registration

3D face registration remains as a critical area of research as it can be applied for image fusion, face recognition, and motion analysis to name a few. It is a challenging task to register a viewpoint face image due to facial occlusion. Generally, the occluded face image due to viewpoint is enhanced...

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Main Author:	Rudrusamy, Bhuvendhraa
Format:	Thesis
Language:	English
Published:	2019
Subjects:	T Technology
Online Access:	http://eprints.usm.my/55400/1/Spherical-Based%20Iterative%20Closest%20Point%20With%20Nose%20Tip%20Localization%20For%20Unconstraint%20Viewpoint%203D%20Face%20Registration.pdf
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spelling	my-usm-ep.554002022-10-20T07:22:03Z Spherical-Based Iterative Closest Point With Nose Tip Localization For Unconstraint Viewpoint 3D Face Registration 2019-04-01 Rudrusamy, Bhuvendhraa T Technology TK Electrical Engineering. Electronics. Nuclear Engineering 3D face registration remains as a critical area of research as it can be applied for image fusion, face recognition, and motion analysis to name a few. It is a challenging task to register a viewpoint face image due to facial occlusion. Generally, the occluded face image due to viewpoint is enhanced and restored using generic image processing techniques prior to face registration. On the other hand, such an approach is approximate. The first contribution of this study is the proposed spherical-based iterative closest point (SICP) for unconstraint viewpoint 3D face registration prior to any kind of image enhancement or restoration. In addition, SICP face registration method able to assist nose tip localization using the genetic algorithm (GA). The proposed SICP+GA method was evaluated on publicly available 3D face database that provides various face poses (including viewpoint up to approximately ±90° yaw angle) and was compared against coherent point drift (CPD) and iterative closest point (ICP) based methods. The experimental finding suggests that the proposed SICP+GA is superior compared to coherent point drift (CPD) and iterative closest point (ICP) based methods, yielding an average mean registration error of 1.0758 mm compared to 1.2465 and 1.3540 mm of later two methods, respectively. More importantly, SICP+GA successfully registered all the frontal and non-frontal images while CPD and ICP failed to register profile posed when the viewpoint yaw angle was about ±90°. In addition, the proposed coarse-to-fine nose tip localization with the assistance of SICP face registration method using GA has improvised the nose tiplocalization distance with an average mean of 1.6767 mm. Also, the experimental finding shows that the proposed technique comes handy when the coarse nose tip annotation is approximate. Contrarily, the face images can be rather noisy and contain outliers which may skew the registration performance. In the effort of investigating the robustness of the proposed SICP face registration method against noise and outliers, post- and pre-filtering methods were performed on SICP face registration method using existing 2.5 2019-04 Thesis http://eprints.usm.my/55400/ http://eprints.usm.my/55400/1/Spherical-Based%20Iterative%20Closest%20Point%20With%20Nose%20Tip%20Localization%20For%20Unconstraint%20Viewpoint%203D%20Face%20Registration.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology T Technology
spellingShingle	T Technology T Technology Rudrusamy, Bhuvendhraa Spherical-Based Iterative Closest Point With Nose Tip Localization For Unconstraint Viewpoint 3D Face Registration
description	3D face registration remains as a critical area of research as it can be applied for image fusion, face recognition, and motion analysis to name a few. It is a challenging task to register a viewpoint face image due to facial occlusion. Generally, the occluded face image due to viewpoint is enhanced and restored using generic image processing techniques prior to face registration. On the other hand, such an approach is approximate. The first contribution of this study is the proposed spherical-based iterative closest point (SICP) for unconstraint viewpoint 3D face registration prior to any kind of image enhancement or restoration. In addition, SICP face registration method able to assist nose tip localization using the genetic algorithm (GA). The proposed SICP+GA method was evaluated on publicly available 3D face database that provides various face poses (including viewpoint up to approximately ±90° yaw angle) and was compared against coherent point drift (CPD) and iterative closest point (ICP) based methods. The experimental finding suggests that the proposed SICP+GA is superior compared to coherent point drift (CPD) and iterative closest point (ICP) based methods, yielding an average mean registration error of 1.0758 mm compared to 1.2465 and 1.3540 mm of later two methods, respectively. More importantly, SICP+GA successfully registered all the frontal and non-frontal images while CPD and ICP failed to register profile posed when the viewpoint yaw angle was about ±90°. In addition, the proposed coarse-to-fine nose tip localization with the assistance of SICP face registration method using GA has improvised the nose tiplocalization distance with an average mean of 1.6767 mm. Also, the experimental finding shows that the proposed technique comes handy when the coarse nose tip annotation is approximate. Contrarily, the face images can be rather noisy and contain outliers which may skew the registration performance. In the effort of investigating the robustness of the proposed SICP face registration method against noise and outliers, post- and pre-filtering methods were performed on SICP face registration method using existing 2.5
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Rudrusamy, Bhuvendhraa
author_facet	Rudrusamy, Bhuvendhraa
author_sort	Rudrusamy, Bhuvendhraa
title	Spherical-Based Iterative Closest Point With Nose Tip Localization For Unconstraint Viewpoint 3D Face Registration
title_short	Spherical-Based Iterative Closest Point With Nose Tip Localization For Unconstraint Viewpoint 3D Face Registration
title_full	Spherical-Based Iterative Closest Point With Nose Tip Localization For Unconstraint Viewpoint 3D Face Registration
title_fullStr	Spherical-Based Iterative Closest Point With Nose Tip Localization For Unconstraint Viewpoint 3D Face Registration
title_full_unstemmed	Spherical-Based Iterative Closest Point With Nose Tip Localization For Unconstraint Viewpoint 3D Face Registration
title_sort	spherical-based iterative closest point with nose tip localization for unconstraint viewpoint 3d face registration
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
publishDate	2019
url	http://eprints.usm.my/55400/1/Spherical-Based%20Iterative%20Closest%20Point%20With%20Nose%20Tip%20Localization%20For%20Unconstraint%20Viewpoint%203D%20Face%20Registration.pdf
_version_	1747822322534842368

Spherical-Based Iterative Closest Point With Nose Tip Localization For Unconstraint Viewpoint 3D Face Registration

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