CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Efficient and Accurate Face Shape Reconstruction by Fusion of Multiple Landmark Databases
Wang, Pengrui1,2; Tian, Yi1,3; Che, Wujun1; Xu, Bo1
2019-09
Conference NameIEEE International Conference on Image Processing (ICIP)
Conference Date2019-9-22~2019-9-25
Conference PlaceTaipei, Taiwan
Abstract

We propose an efficient and accurate regression-based 3D face shape reconstruction method. We use an encoder based on MobileNet to estimate parameters including face pose and coefficients of a parametric face model from a single face image. The encoder is trained only by 2D landmarks. Faces can be reconstructed by these parameters. Three contributions of our method are: 1) we propose a databases fusion method to train our network which can easily utilize multiple 2D landmark databases which have different landmark numbers and positions; 2) with the fusion method, we propose a simple MobileNet based network which is efficient, accurate and robust for face reconstruction even without complex training strategies; 3) we add an additional deformation field for shape correction to further improve our network's performance. Experiments demonstrate our method can bring about great performance improvement on most test databases and also compare favorably to some state-of-the-art methods in performance and speed.

Keyword3D Face Reconstruction Multi Database Fusion Face Alignment Deep Learning
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40397
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorChe, Wujun
Affiliation1.Institute of Automation, Chinese Academy of Sciences, China
2.Hunan Normal University, China
3.University of Chinese Academy of Sciences, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Pengrui,Tian, Yi,Che, Wujun,et al. Efficient and Accurate Face Shape Reconstruction by Fusion of Multiple Landmark Databases[C],2019.
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