Relative coordinates constraint for face alignment
文献类型：期刊论文
| 作者 | Nian, Fudong2; Li, Teng1; Bao, Bing-Kun3 ; Xu, Changsheng4
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2020-06-28 |
| 卷号 | 395页码:119-127 |
| 关键词 | Face alignment Relative coordinates constraint CNN Loss function design |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2017.12.071 |
| 通讯作者 | Bao, Bing-Kun(bingkunbao@njput.edu.cn) |
| 英文摘要 | We present a practical approach to improve the precision of face alignment for a single image. Recently, face alignment is deemed as a regression problem, and convolutional neural networks (CNNs) or recurrent neural networks (RNNs) are utilized to predict the coordinates of facial landmarks. However, most existing methods only adopt Euclidean loss as the optimization target for each landmark, and neglect the correlations between them, which we think may be inappropriate. To address this issue, in this paper, we introduce a novel Relative Coordinates Constraint (RCC) loss function for face alignment, which considers the relative coordinates between any pairs of landmarks as a new supervision signal. More importantly, we prove that the proposed RCC loss function is trainable and can be easily incorporated in existing CNNs optimization procedure. With the joint supervision of Euclidean loss and RCC loss, we train a robust and light CNNs framework for face alignment. Extensive experimental results on several datasets show that the precision of face alignment improved significantly by the proposed RCC loss and quantitative results are comparable to state-of-the-art methods (mean error 5.39 on 300-W and 6.99 on AFLW). In addition, the proposed framework is also an efficient solution (300 FPS on CPU). We share the implementation code of our proposed methods at https://github.com/nianfudong/RCC-loss. (C) 2019 Elsevier B.V. All rights reserved. |
| 资助项目 | National Natural Science Foundation of China[61572503] ; National Natural Science Foundation of China[61572029] ; National Natural Science Foundation of China[61872424] ; National Natural Science Foundation of China[61930 00388] ; National Natural Science Foundation of China[6190070645] ; National Key R&D Program of China[2018YFB1305804] ; Scientific Research Development Foundation of Hefei University[19ZR15ZDA] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; NUPTSF[NY218001] ; Talent Research Foundation of Hefei University[16-17RC23] ; Talent Research Foundation of Hefei University[18-19RC54] ; Open fund for Discipline Construction, Institute of Physical Science and Information Technology, Anhui University |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000536809600012 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; National Key R&D Program of China ; Scientific Research Development Foundation of Hefei University ; Key Research Program of Frontier Sciences, CAS ; NUPTSF ; Talent Research Foundation of Hefei University ; Open fund for Discipline Construction, Institute of Physical Science and Information Technology, Anhui University |
| 源URL | [http://ir.ia.ac.cn/handle/173211/39564]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 通讯作者 | Bao, Bing-Kun |
| 作者单位 | 1.Anhui Univ, Hefei, Peoples R China 2.Hefei Univ, Hefei, Peoples R China 3.Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Nian, Fudong,Li, Teng,Bao, Bing-Kun,et al. Relative coordinates constraint for face alignment[J]. NEUROCOMPUTING,2020,395:119-127. |
| APA | Nian, Fudong,Li, Teng,Bao, Bing-Kun,&Xu, Changsheng.(2020).Relative coordinates constraint for face alignment.NEUROCOMPUTING,395,119-127. |
| MLA | Nian, Fudong,et al."Relative coordinates constraint for face alignment".NEUROCOMPUTING 395(2020):119-127. |
入库方式： OAI收割
来源：自动化研究所
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