Annotated face model-based alignment: a robust landmark-free pose estimation approach for 3D model registration. Y Wu, SK Shah, IA Kakadiaris.
Date: November 2017.
Source: Machine Vision and Applications (2017). https://doi.org/10.1007/s00138-017-0887-6.
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 ×× 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.
Article: Annotated face model-based alignment: a robust landmark-free pose estimation approach for 3D model registration.
Authors: Yuhang Wu; Shishir K Shah; Ioannis A Kakadiaris. Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, USA.