Nose tip region detection in 3D facial model across large pose variation and facial expression

Detecting nose tip location has become an important task in face analysis. However, for a 3D face model with presence of large rotation variation, detecting nose tip location is certainly a challenging task. In this paper, we propose a method to detect nose tip region in large rotation variation bas...

Full description

Saved in:
Bibliographic Details
Main Authors: Anuar, Laili Hayati, Mashohor, Syamsiah, Mokhtar, Makhfudzah, Wan Adnan, Wan Azizun
Format: Article
Language:English
English
Published: 2010
Online Access:http://psasir.upm.edu.my/id/eprint/15836/1/Nose%20tip%20region%20detection%20in%203D%20facial%20model%20across%20large%20pose%20variation%20and%20facial%20expression.pdf
http://psasir.upm.edu.my/id/eprint/15836/
http://ijcsi.org/articles/Nose-Tip-Region-Detection-in-3D-Facial-Model-across-Large-Pose-Variation-and-Facial-Expression.php
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.15836
record_format eprints
spelling my.upm.eprints.158362015-10-23T02:33:38Z http://psasir.upm.edu.my/id/eprint/15836/ Nose tip region detection in 3D facial model across large pose variation and facial expression Anuar, Laili Hayati Mashohor, Syamsiah Mokhtar, Makhfudzah Wan Adnan, Wan Azizun Detecting nose tip location has become an important task in face analysis. However, for a 3D face model with presence of large rotation variation, detecting nose tip location is certainly a challenging task. In this paper, we propose a method to detect nose tip region in large rotation variation based on the geometrical shape of a nose. Nose region has always been considered as the most protuberant part of a face. Based on convex points of face surface, we use morphological approach to obtain nose tip region candidates consist of highest point density. For each point of each region candidate, a signature is generated and evaluated with trained nose tip tolerance band for matching purpose. The region that contains the point which scores the most is chosen as the final nose tip region. This method can handle large rotation variation, facial expression, combination of all rotations (yaw, pitch and roll) and large non-facial outliers. Combination of two databases has been used; UPMFace and GavabDB as training data set and test data set. The experimental results show that 95.19% nose tip region over 1300 3D face models were correctly detected. 2010-07 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/15836/1/Nose%20tip%20region%20detection%20in%203D%20facial%20model%20across%20large%20pose%20variation%20and%20facial%20expression.pdf Anuar, Laili Hayati and Mashohor, Syamsiah and Mokhtar, Makhfudzah and Wan Adnan, Wan Azizun (2010) Nose tip region detection in 3D facial model across large pose variation and facial expression. IJCSI International Journal of Computer Science Issues, 7 (4). pp. 1-9. ISSN 1694-0814; ESSN: 1694-0784 http://ijcsi.org/articles/Nose-Tip-Region-Detection-in-3D-Facial-Model-across-Large-Pose-Variation-and-Facial-Expression.php English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description Detecting nose tip location has become an important task in face analysis. However, for a 3D face model with presence of large rotation variation, detecting nose tip location is certainly a challenging task. In this paper, we propose a method to detect nose tip region in large rotation variation based on the geometrical shape of a nose. Nose region has always been considered as the most protuberant part of a face. Based on convex points of face surface, we use morphological approach to obtain nose tip region candidates consist of highest point density. For each point of each region candidate, a signature is generated and evaluated with trained nose tip tolerance band for matching purpose. The region that contains the point which scores the most is chosen as the final nose tip region. This method can handle large rotation variation, facial expression, combination of all rotations (yaw, pitch and roll) and large non-facial outliers. Combination of two databases has been used; UPMFace and GavabDB as training data set and test data set. The experimental results show that 95.19% nose tip region over 1300 3D face models were correctly detected.
format Article
author Anuar, Laili Hayati
Mashohor, Syamsiah
Mokhtar, Makhfudzah
Wan Adnan, Wan Azizun
spellingShingle Anuar, Laili Hayati
Mashohor, Syamsiah
Mokhtar, Makhfudzah
Wan Adnan, Wan Azizun
Nose tip region detection in 3D facial model across large pose variation and facial expression
author_facet Anuar, Laili Hayati
Mashohor, Syamsiah
Mokhtar, Makhfudzah
Wan Adnan, Wan Azizun
author_sort Anuar, Laili Hayati
title Nose tip region detection in 3D facial model across large pose variation and facial expression
title_short Nose tip region detection in 3D facial model across large pose variation and facial expression
title_full Nose tip region detection in 3D facial model across large pose variation and facial expression
title_fullStr Nose tip region detection in 3D facial model across large pose variation and facial expression
title_full_unstemmed Nose tip region detection in 3D facial model across large pose variation and facial expression
title_sort nose tip region detection in 3d facial model across large pose variation and facial expression
publishDate 2010
url http://psasir.upm.edu.my/id/eprint/15836/1/Nose%20tip%20region%20detection%20in%203D%20facial%20model%20across%20large%20pose%20variation%20and%20facial%20expression.pdf
http://psasir.upm.edu.my/id/eprint/15836/
http://ijcsi.org/articles/Nose-Tip-Region-Detection-in-3D-Facial-Model-across-Large-Pose-Variation-and-Facial-Expression.php
_version_ 1643826043934474240
score 13.211869