Research Article
Face Reconstruction with Specific Weight Mask
@INPROCEEDINGS{10.1007/978-3-030-77569-8_17, author={Wentao Shi and Tianji Ma and Nangyang Bai and Lutao Wang}, title={Face Reconstruction with Specific Weight Mask}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 16th EAI International Conference, QShine 2020, Virtual Event, November 29--30, 2020, Proceedings}, proceedings_a={QSHINE}, year={2021}, month={6}, keywords={Computer vision Face alignment Dense alignment Face reconstruction}, doi={10.1007/978-3-030-77569-8_17} }- Wentao Shi
Tianji Ma
Nangyang Bai
Lutao Wang
Year: 2021
Face Reconstruction with Specific Weight Mask
QSHINE
Springer
DOI: 10.1007/978-3-030-77569-8_17
Abstract
Create a 3D face model from a 2D face image, generally extract facial feature points, calculate a 3D deformation model, and perform deformation and stretching on the generated face database. However, this approach is not only time-consuming but also has no calculation errors. Ideally, neural networks’ use to obtain deformation model parameters is also affected by factors such as pose, angle, and datasets. 3D face reconstruction methods rely excessively on the accuracy of the labeling and the face detector’s accuracy. This article proposes a method that is not affected by pose. We adopt a feature point extractor that can obtain more features, design an hourglass network to get a model, and consider each feature area differently, effectively using the feature point information. Map from two-dimensional coordinates to three-dimensional space to achieve face reconstruction, and obtain a high-precision face model. We do experiments on the three-dimensional face datasets AFLW2000-3D and 300W-3D. The results show that this method can obtain good performance in face multi-angle reconstruction, and the accuracy is also improved.
