Recognizing Profile Faces by Imagining Frontal View
文献类型：期刊论文
| 作者 | Zhao, Jian5; Xing, Junliang2 ; Xiong, Lin3; Yan, Shuicheng1,4; Feng, Jiashi1
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| 刊名 | INTERNATIONAL JOURNAL OF COMPUTER VISION
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| 出版日期 | 2019-11-05 |
| 页码 | 19 |
| 关键词 | Pose-invariant face recognition Face frontalization Cross-domain adversarial learning Meta-learning Learning to learn Enforced cross-entropy optimization Generative adversarial networks |
| ISSN号 | 0920-5691 |
| DOI | 10.1007/s11263-019-01252-7 |
| 通讯作者 | Xing, Junliang(jlxing@nlpr.ia.ac.cn) |
| 英文摘要 | Extreme pose variation is one of the key obstacles to accurate face recognition in practice. Compared with current techniques for pose-invariant face recognition, which either expect pose invariance from hand-crafted features or data-driven deep learning solutions, or first normalize profile face images to frontal pose before feature extraction, we argue that it is more desirable to perform both tasks jointly to allow them to benefit from each other. To this end, we propose a Pose-Invariant Model (PIM) for face recognition in the wild, with three distinct novelties. First, PIM is a novel and unified deep architecture, containing a Face Frontalization sub-Net (FFN) and a Discriminative Learning sub-Net (DLN), which are jointly learned from end to end. Second, FFN is a well-designed dual-path Generative Adversarial Network which simultaneously perceives global structures and local details, incorporating an unsupervised cross-domain adversarial training and a meta-learning ("learning to learn") strategy using siamese discriminator with dynamic convolution for high-fidelity and identity-preserving frontal view synthesis. Third, DLN is a generic Convolutional Neural Network (CNN) for face recognition with our enforced cross-entropy optimization strategy for learning discriminative yet generalized feature representations with large intra-class affinity and inter-class separability. Qualitative and quantitative experiments on both controlled and in-the-wild benchmark datasets demonstrate the superiority of the proposed model over the state-of-the-arts. |
| WOS关键词 | RECOGNITION |
| 资助项目 | National Science Foundation of China[61672519] ; NUS startup[R-263-000-C08-133] ; NUS IDS[R-263-000-C67-646] ; ECRA[R-263-000-C87-133] ; MOE[R-263-000-C21-112] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000494408100001 |
| 出版者 | SPRINGER |
| 资助机构 | National Science Foundation of China ; NUS startup ; NUS IDS ; ECRA ; MOE |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28872]  |
| 专题 | 智能系统与工程 |
| 通讯作者 | Xing, Junliang |
| 作者单位 | 1.Natl Univ Singapore, Singapore, Singapore 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.JD Digits, Silicon Valley, CA USA 4.Yitu Technol, Beijing, Peoples R China 5.Inst North Elect Equipment, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Jian,Xing, Junliang,Xiong, Lin,et al. Recognizing Profile Faces by Imagining Frontal View[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2019:19. |
| APA | Zhao, Jian,Xing, Junliang,Xiong, Lin,Yan, Shuicheng,&Feng, Jiashi.(2019).Recognizing Profile Faces by Imagining Frontal View.INTERNATIONAL JOURNAL OF COMPUTER VISION,19. |
| MLA | Zhao, Jian,et al."Recognizing Profile Faces by Imagining Frontal View".INTERNATIONAL JOURNAL OF COMPUTER VISION (2019):19. |
入库方式： OAI收割
来源：自动化研究所
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