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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
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2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/15836/ 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 |
| _version_ | 1848842790978453504 |
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| author | Anuar, Laili Hayati Mashohor, Syamsiah Mokhtar, Makhfudzah Wan Adnan, Wan Azizun |
| author_facet | Anuar, Laili Hayati Mashohor, Syamsiah Mokhtar, Makhfudzah Wan Adnan, Wan Azizun |
| author_sort | Anuar, Laili Hayati |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T08:04:44Z |
| format | Article |
| id | upm-15836 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T08:04:44Z |
| publishDate | 2010 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/15836/ http://psasir.upm.edu.my/id/eprint/15836/ 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 |