IEICE TRANSACTIONS on Information
3D Face Landmarking Method under Pose and Expression Variations
Summary :
A robust method is presented for 3D face landmarking with facial pose and expression variations. This method is based on Multi-level Partition of Unity (MPU) Implicits without relying on texture, pose, orientation and expression information. The MPU Implicits reconstruct 3D face surface in a hierarchical way. From lower to higher reconstruction levels, the local shapes can be reconstructed gradually according to their significance. For 3D faces, three landmarks, nose, left eyehole and right eyehole, can be detected uniquely with the analysis of curvature features at lower levels. Experimental results on GavabDB database show that this method is invariant to pose, holes, noise and expression. The overall performance of 98.59% is achieved under pose and expression variations.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.3 pp.729-733
- Publication Date
- 2011/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.729
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Yuan HU, Jingqi YAN, Wei LI, Pengfei SHI, "3D Face Landmarking Method under Pose and Expression Variations" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 729-733, March 2011, doi: 10.1587/transinf.E94.D.729.
Abstract: A robust method is presented for 3D face landmarking with facial pose and expression variations. This method is based on Multi-level Partition of Unity (MPU) Implicits without relying on texture, pose, orientation and expression information. The MPU Implicits reconstruct 3D face surface in a hierarchical way. From lower to higher reconstruction levels, the local shapes can be reconstructed gradually according to their significance. For 3D faces, three landmarks, nose, left eyehole and right eyehole, can be detected uniquely with the analysis of curvature features at lower levels. Experimental results on GavabDB database show that this method is invariant to pose, holes, noise and expression. The overall performance of 98.59% is achieved under pose and expression variations.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.729/_p
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@ARTICLE{e94-d_3_729,
author={Yuan HU, Jingqi YAN, Wei LI, Pengfei SHI, },
journal={IEICE TRANSACTIONS on Information},
title={3D Face Landmarking Method under Pose and Expression Variations},
year={2011},
volume={E94-D},
number={3},
pages={729-733},
abstract={A robust method is presented for 3D face landmarking with facial pose and expression variations. This method is based on Multi-level Partition of Unity (MPU) Implicits without relying on texture, pose, orientation and expression information. The MPU Implicits reconstruct 3D face surface in a hierarchical way. From lower to higher reconstruction levels, the local shapes can be reconstructed gradually according to their significance. For 3D faces, three landmarks, nose, left eyehole and right eyehole, can be detected uniquely with the analysis of curvature features at lower levels. Experimental results on GavabDB database show that this method is invariant to pose, holes, noise and expression. The overall performance of 98.59% is achieved under pose and expression variations.},
keywords={},
doi={10.1587/transinf.E94.D.729},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - 3D Face Landmarking Method under Pose and Expression Variations
T2 - IEICE TRANSACTIONS on Information
SP - 729
EP - 733
AU - Yuan HU
AU - Jingqi YAN
AU - Wei LI
AU - Pengfei SHI
PY - 2011
DO - 10.1587/transinf.E94.D.729
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - A robust method is presented for 3D face landmarking with facial pose and expression variations. This method is based on Multi-level Partition of Unity (MPU) Implicits without relying on texture, pose, orientation and expression information. The MPU Implicits reconstruct 3D face surface in a hierarchical way. From lower to higher reconstruction levels, the local shapes can be reconstructed gradually according to their significance. For 3D faces, three landmarks, nose, left eyehole and right eyehole, can be detected uniquely with the analysis of curvature features at lower levels. Experimental results on GavabDB database show that this method is invariant to pose, holes, noise and expression. The overall performance of 98.59% is achieved under pose and expression variations.
ER -
English
