Multi-objective evolutionary 3D face reconstruction based on improved encoder-decoder network
文献类型：期刊论文
| 作者 | Cai, Xingjuan3; Cao, Yihao3; Ren, Yeqing2; Cui, Zhihua3; Zhang, Wensheng1
|
| 刊名 | INFORMATION SCIENCES
 |
| 出版日期 | 2021-12-01 |
| 卷号 | 581页码:233-248 |
| ISSN号 | 0020-0255 |
| 关键词 | 3D face reconstruction Multi-objective evolutionary Regularization algorithm Computer vision |
| DOI | 10.1016/j.ins.2021.09.024 |
| 通讯作者 | Cui, Zhihua(zhihua.cui@hotmail.com) |
| 英文摘要 | Three-dimensional (3D) face application has attracted significant attention the field of multimedia and image processing. However, the end-to-end 3D face reconstruction method is still immature, and there are some problems, such as overfitting caused by too few training sets and unacceptable 3D face texture alignment performance. Therefore, we design a novel approach to construct a 3D face, named multi-objective evo-lutionary 3D face reconstruction based on improved encoder-decoder network (MoEDN). This study introduces a regularization algorithm named feature map distortion (Disout); whose purpose is to strengthen the network generalization ability. Based on this, we con-struct a multi-objective evolutionary 3D face reconstruction model, in which decision vari-ables are distortion probability, distorted block size, distorted intensity, probability step, and learning rate; and objective functions are loss and structural similarity (SSIM). We use four multi-objective evolutionary algorithms (NSGA-II, AGEII, NSLS, and MOEA/D) to optimize the proposed model. Experimental results demonstrate that NSLS has the best performance. In addition, compared with position map regression network (PRNet), 2D-assisted self-supervised learning (2DASL) and other state-of-the-art, the proposed model achieves better loss values and NME values. Therefore, the proposed multi-objective evo-lutionary 3D face reconstruction model has outstanding 3D facial reconstruction perfor-mance in large poses and face expression. (c) 2021 Elsevier Inc. All rights reserved. |
| WOS关键词 | IMAGE ; ALGORITHM |
| 资助项目 | National Key Research and Development Program of China[2018YFC1604000] ; National Natural Science Foundation of China[61806138] ; Key R&D program of Shanxi Province (International Cooperation)[201903D421048] ; Key R&D program of Shanxi Province (High Technology)[201903D121119] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:000705058500005 |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key R&D program of Shanxi Province (International Cooperation) ; Key R&D program of Shanxi Province (High Technology) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46203]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Cui, Zhihua |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China 3.Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cai, Xingjuan,Cao, Yihao,Ren, Yeqing,et al. Multi-objective evolutionary 3D face reconstruction based on improved encoder-decoder network[J]. INFORMATION SCIENCES,2021,581:233-248. |
| APA | Cai, Xingjuan,Cao, Yihao,Ren, Yeqing,Cui, Zhihua,&Zhang, Wensheng.(2021).Multi-objective evolutionary 3D face reconstruction based on improved encoder-decoder network.INFORMATION SCIENCES,581,233-248. |
| MLA | Cai, Xingjuan,et al."Multi-objective evolutionary 3D face reconstruction based on improved encoder-decoder network".INFORMATION SCIENCES 581(2021):233-248. |
入库方式： OAI收割
来源：自动化研究所
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