The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Um método robusto é apresentado para marcação facial 3D com variações de pose e expressão facial. Este método é baseado em Implícitos de Partição de Unidade de Vários Níveis (MPU) sem depender de informações de textura, pose, orientação e expressão. Os MPU Implicits reconstroem a superfície da face 3D de forma hierárquica. Dos níveis de reconstrução mais baixos para os mais altos, as formas locais podem ser reconstruídas gradualmente de acordo com o seu significado. Para faces 3D, três pontos de referência, nariz, olho esquerdo e olho direito, podem ser detectados exclusivamente com a análise de características de curvatura em níveis mais baixos. Resultados experimentais no banco de dados GavabDB mostram que este método é invariante a pose, buracos, ruído e expressão. O desempenho geral de 98.59% é alcançado sob variações de pose e expressão.
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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},}
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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 -