3D face reconstruction via landmark depth estimation and shape deformation
详细信息 查看全文
- 作者：Peizhong Liu ; Ming Hong ; Minghang Wang ; Peiting Gu…
- 关键词：3D face reconstruction ; Sparse representation ; Weighted l1 norm sparsity ; Shape deformation
- 刊名：Multimedia Tools and Applications
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：76
- 期：2
- 页码：2749-2767
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
- 出版者：Springer US
- ISSN：1573-7721
- 卷排序：76
文摘
Since a human face could be represented by a few landmarks with less redundant information, and calculated by a linear combination of a small number of prototypical faces, we propose a two-step 3D face reconstruction approach including landmark depth estimation and shape deformation. The proposed approach allows us to reconstruct a realistic 3D face from a 2D frontal face image. First, we apply a coupled dictionary learning method based on sparse representation to explore the underlying mappings between pair of 2D and 3D training landmarks. In the method, a weighted l1 norm sparsity function is introduced to better pursuit the l0 norm sparsity. Then, the depth of the landmarks could be estimated. Second, we propose a novel shape deformation method to reconstruct the 3D face by combining a small number of most relevant deformed faces which are obtained by the estimated landmarks. The sparsity regulation is also introduced to find the relevant faces in the second step. The proposed approach could explore the distributions of 2D and 3D faces and the underlying mappings between them well, because human faces are represented by low-dimensional landmarks, and their distributions are described by sparse representations. Moreover, it is much more flexible since we can make any change in any step. Extensive experiments are conducted on BJUT_3D database, and the results validate the effectiveness of the proposed approach.