Paper Info
Title
Depth-Based 3D Face Reconstruction and Pose Estimation Using Shape-Preserving Domain Adaptation
Abstract
Depth images are widely used in 3D head pose estimation and face reconstruction. The device-specific noise and the lack of textual constraints pose a major problem for estimating a nonrigid deformable face from a single noisy depth image. In this article, we present a deep neural network-based framework to infer a 3D face consistent with a single depth image captured by a consumer depth camera Kinect. Confronted with a lack of annotated depth images with facial parameters, we utilize the bidirectional CycleGAN-based generator for denoising and noisy image simulation, which helps to generalize the model learned from synthetic depth images to real noisy ones. We generate the code regressors in the source (synthetic) and the target (noisy) depth image domains and present a fusion scheme in the parametric space for 3D face inference. The proposed multi-level shape consistency constraint, concerning the embedded features, depth maps, and 3D surfaces, couples the code regressor and the domain adaptation, avoiding shape distortions in the CycleGAN-based generators. Experiments demonstrate that the proposed method is effective in depth-based 3D head pose estimation and expressive face reconstruction compared with the state-of-the-art.
Year
DOI
Venue
2021
10.1109/TBIOM.2020.3025466
IEEE Transactions on Biometrics, Behavior, and Identity Science
Keywords
DocType
Volume
Depth-based face reconstruction,shape-preserving domain adaptation,pose estimation,shape code regression
Journal
3
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Yicheng Zhong100.34
Yuru Pei210115.45
Peixin Li302.03
Yuke Guo436.14
Gengyu Ma596.01
Meng Liu600.34
Wei Bai7309.28
Wenhai Wu800.34
Hongbin Zha92206183.36