Knowledge Commons of Institute of Automation，CAS
| Label Distribution-Based Facial Attractiveness Computation by Deep Residual Learning | |
Fan, Yang-Yu1; Liu, Shu1 ; Li, Bo1; Guo, Zhe1; Samal, Ashok2; Wan, Jun3,4; Li, Stan Z.3,4
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| 发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA
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| ISSN | 1520-9210 |
| 2018-08-01 | |
| 卷号 | 20期号:8页码:2196-2208 |
| 通讯作者 | Liu, Shu(liushu0922@mail.nwpu.edu.cn) |
| 摘要 | Two key challenges lie in the facial attractiveness computation research: the lack of discriminative face representations, and the scarcity of sufficient and complete training data. Motivated by recent promising work in face recognition using deep neural networks to learn effective features, the first challenge is expected to be addressed from a deep learning point of view. A very deep residual network is utilized to enable automatic learning of hierarchical aesthetics representation. The inspiration to deal with the second challenge comes from the natural representation of the training data, where each training face can be associated with a label (score) distribution given by human raters rather than a single label (average score). This paper, therefore, recasts facial attractiveness computation as a label distribution learning problem. Integrating these two ideas, an end-to-end attractiveness learning framework is established. We also perform feature-level fusion by incorporating the low-level geometric features to further improve the computational performance. Extensive experiments are conducted on a standard benchmark, the SCUT-FBP dataset, where our approach shows significant advantages over the other state-of-the-art work. |
| 关键词 | Facial attractiveness computation deep residual network label distribution feature fusion SCUT-FBP |
| DOI | 10.1109/TMM.2017.2780762 |
| 关键词[WOS] | BEAUTY ; PREDICTION |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | National Natural Science Foundation of China[61402371] ; National Natural Science Foundation of China[61461025] ; National Natural Science Foundation of China[61702462] ; National Natural Science Foundation of China[61502491] ; Science and Technology Innovation Engineering Plan in Shaanxi Province of China[2013SZS15-K02] ; Natural Science Basic Research Plan in Shaanxi Province of China[2017JM6008] ; National Natural Science Foundation of China[61402371] ; National Natural Science Foundation of China[61461025] ; National Natural Science Foundation of China[61702462] ; National Natural Science Foundation of China[61502491] ; Science and Technology Innovation Engineering Plan in Shaanxi Province of China[2013SZS15-K02] ; Natural Science Basic Research Plan in Shaanxi Province of China[2017JM6008] |
| 项目资助者 | National Natural Science Foundation of China ; Science and Technology Innovation Engineering Plan in Shaanxi Province of China ; Natural Science Basic Research Plan in Shaanxi Province of China |
| WOS研究方向 | Computer Science ; Telecommunications |
| WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
| WOS记录号 | WOS:000439378600022 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/19773 |
| 专题 | 多模态人工智能系统全国重点实验室_生物识别与安全技术 |
| 通讯作者 | Liu, Shu |
| 作者单位 | 1.Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Shaanxi, Peoples R China 2.Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA 3.Chinese Acad Sci, Ctr Biometr & Secur Res, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fan, Yang-Yu,Liu, Shu,Li, Bo,et al. Label Distribution-Based Facial Attractiveness Computation by Deep Residual Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2018,20(8):2196-2208. |
| APA | Fan, Yang-Yu.,Liu, Shu.,Li, Bo.,Guo, Zhe.,Samal, Ashok.,...&Li, Stan Z..(2018).Label Distribution-Based Facial Attractiveness Computation by Deep Residual Learning.IEEE TRANSACTIONS ON MULTIMEDIA,20(8),2196-2208. |
| MLA | Fan, Yang-Yu,et al."Label Distribution-Based Facial Attractiveness Computation by Deep Residual Learning".IEEE TRANSACTIONS ON MULTIMEDIA 20.8(2018):2196-2208. |
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| TMM2017_attractive.p（1377KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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