A Non-template based Automatic Landmarking on 3-Dimensional (3D) Faces

Face landmarking is an important task in many face image processing approaches. Detecting and localising landmarks from face data are often performed manually by trained and experienced experts. The appearance of facial landmarks may vary tremendously due to facial expressions (such as opened/closed...

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Bibliographic Details
Main Author:	Pui, Suk Ting
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA75 Electronic computers Computer science QA76 Computer software
Online Access:	http://ir.unimas.my/id/eprint/38326/1/Pui%20Suk%20Ting%20ft.pdf
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id	my-unimas-ir.38326
record_format	uketd_dc
spelling	my-unimas-ir.383262023-06-21T06:53:52Z A Non-template based Automatic Landmarking on 3-Dimensional (3D) Faces 2022 Pui, Suk Ting QA75 Electronic computers. Computer science QA76 Computer software Face landmarking is an important task in many face image processing approaches. Detecting and localising landmarks from face data are often performed manually by trained and experienced experts. The appearance of facial landmarks may vary tremendously due to facial expressions (such as opened/closed eyes and mouth) and pose variations. Therefore, this process is not only laborious but also prone to inaccuracies. The developed automatic landmarking model is currently not robust in such unconditional environments. A novel non-template based automatic landmarking model on 3D face data is presented. This can work robustly on faces with variants of facial expressions at different intensities. This model is referred as Mean-Bound-Cluster Automatic Face Landmarking in 3D, MBC-AFL3D. It consists of two main processes which are detection and localisation in the model. The detection process involves mean (H) surface curvature and 3D bounding box segmentation to extract distinct features from face, while the localisation process implies K-means Clustering to search for the best landmark of the face. Experiments show that landmarks found with MBC-AFL3D are on average between 3.17 mm to 9.78 mm as compared to manual landmarking. It is also demonstrated robustness of its method on landmarking faces with high expression variations even with mouth opened. The MBC-AFL3D has improved the accuracy and performance on automatic 3D face landmarking with face expressions. Universiti Malaysia Sarawak (UNIMAS) 2022 Thesis http://ir.unimas.my/id/eprint/38326/ http://ir.unimas.my/id/eprint/38326/1/Pui%20Suk%20Ting%20ft.pdf text en validuser phd doctoral Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers Computer science QA76 Computer software Pui, Suk Ting A Non-template based Automatic Landmarking on 3-Dimensional (3D) Faces
description	Face landmarking is an important task in many face image processing approaches. Detecting and localising landmarks from face data are often performed manually by trained and experienced experts. The appearance of facial landmarks may vary tremendously due to facial expressions (such as opened/closed eyes and mouth) and pose variations. Therefore, this process is not only laborious but also prone to inaccuracies. The developed automatic landmarking model is currently not robust in such unconditional environments. A novel non-template based automatic landmarking model on 3D face data is presented. This can work robustly on faces with variants of facial expressions at different intensities. This model is referred as Mean-Bound-Cluster Automatic Face Landmarking in 3D, MBC-AFL3D. It consists of two main processes which are detection and localisation in the model. The detection process involves mean (H) surface curvature and 3D bounding box segmentation to extract distinct features from face, while the localisation process implies K-means Clustering to search for the best landmark of the face. Experiments show that landmarks found with MBC-AFL3D are on average between 3.17 mm to 9.78 mm as compared to manual landmarking. It is also demonstrated robustness of its method on landmarking faces with high expression variations even with mouth opened. The MBC-AFL3D has improved the accuracy and performance on automatic 3D face landmarking with face expressions.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Pui, Suk Ting
author_facet	Pui, Suk Ting
author_sort	Pui, Suk Ting
title	A Non-template based Automatic Landmarking on 3-Dimensional (3D) Faces
title_short	A Non-template based Automatic Landmarking on 3-Dimensional (3D) Faces
title_full	A Non-template based Automatic Landmarking on 3-Dimensional (3D) Faces
title_fullStr	A Non-template based Automatic Landmarking on 3-Dimensional (3D) Faces
title_full_unstemmed	A Non-template based Automatic Landmarking on 3-Dimensional (3D) Faces
title_sort	non-template based automatic landmarking on 3-dimensional (3d) faces
granting_institution	Universiti Malaysia Sarawak
granting_department	Faculty of Computer Science and Information Technology
publishDate	2022
url	http://ir.unimas.my/id/eprint/38326/1/Pui%20Suk%20Ting%20ft.pdf
_version_	1783728500821196800

A Non-template based Automatic Landmarking on 3-Dimensional (3D) Faces

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