Annotated face model-based alignment: a robust landmark-free pose estimation approach for 3D model registration

Annotated face model-based alignment: a robust landmark-free pose estimation approach for 3D... Registering a 3D facial model onto a 2D image is important for constructing pixel-wise correspondences between different facial images. The registration is based on a 3 $$\times $$ × 4 dimensional projection matrix, which is obtained from pose estimation. Conventional pose estimation approaches employ facial landmarks to determine the coefficients inside the projection matrix and are sensitive to missing or incorrect landmarks. In this paper, a landmark-free pose estimation method is presented. The method can be used to estimate the matrix when facial landmarks are not available. Experimental results show that the proposed method outperforms several landmark-free pose estimation methods and achieves competitive accuracy in terms of estimating pose parameters. The method is also demonstrated to be effective as part of a 3D-aided face recognition pipeline (UR2D), whose rank-1 identification rate is competitive to the methods that use landmarks to estimate head pose. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Vision and Applications Springer Journals

Annotated face model-based alignment: a robust landmark-free pose estimation approach for 3D model registration

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany
Subject
Computer Science; Pattern Recognition; Image Processing and Computer Vision; Communications Engineering, Networks
ISSN
0932-8092
eISSN
1432-1769
D.O.I.
10.1007/s00138-017-0887-6
Publisher site
See Article on Publisher Site

Abstract

Registering a 3D facial model onto a 2D image is important for constructing pixel-wise correspondences between different facial images. The registration is based on a 3 $$\times $$ × 4 dimensional projection matrix, which is obtained from pose estimation. Conventional pose estimation approaches employ facial landmarks to determine the coefficients inside the projection matrix and are sensitive to missing or incorrect landmarks. In this paper, a landmark-free pose estimation method is presented. The method can be used to estimate the matrix when facial landmarks are not available. Experimental results show that the proposed method outperforms several landmark-free pose estimation methods and achieves competitive accuracy in terms of estimating pose parameters. The method is also demonstrated to be effective as part of a 3D-aided face recognition pipeline (UR2D), whose rank-1 identification rate is competitive to the methods that use landmarks to estimate head pose.

Journal

Machine Vision and ApplicationsSpringer Journals

Published: Nov 30, 2017

References

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