Graph Jigsaw Learning for Cartoon Face Recognition
文献类型：期刊论文
| 作者 | Li, Yong1,2; Lao, Lingjie1,2; Cui, Zhen1,2; Shan, Shiguang3,4,5; Yang, Jian1,2 |
| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| 出版日期 | 2022 |
| 卷号 | 31页码:3961-3972 |
| ISSN号 | 1057-7149 |
| 关键词 | Face recognition Shape Training Image color analysis Layout Convolutional neural networks Task analysis Cartoon face recognition jigsaw solving graph convolutional network self-supervised learning |
| DOI | 10.1109/TIP.2022.3177952 |
| 英文摘要 | Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognizing cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner. Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually. GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time. Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements. |
| 资助项目 | National Key Research and Development Program of China[2017YFA0700800] ; National Natural Science Foundation of China[62102180] ; National Natural Science Foundation of China[62072244] ; Natural Science Foundation of Jiangsu Province[BK20210329] ; Fundamental Research Funds for the Central Universities[30921011104] ; Natural Science Foundation of Shandong Province[ZR2020LZH008] ; State Key Laboratory of High-End Server and Storage Technology |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000809404700007 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19612]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Cui, Zhen |
| 作者单位 | 1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, PCA Lab,Minist Educ, Key Lab Intelligent Percept & Syst High Dimens In, Nanjing 210094, Peoples R China 2.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Jiangsu Key Lab Image & Video Understanding Socia, Nanjing 210094, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 5.Peng Cheng Lab, Shenzhen 518055, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Yong,Lao, Lingjie,Cui, Zhen,et al. Graph Jigsaw Learning for Cartoon Face Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:3961-3972. |
| APA | Li, Yong,Lao, Lingjie,Cui, Zhen,Shan, Shiguang,&Yang, Jian.(2022).Graph Jigsaw Learning for Cartoon Face Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,3961-3972. |
| MLA | Li, Yong,et al."Graph Jigsaw Learning for Cartoon Face Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):3961-3972. |
入库方式： OAI收割
来源：计算技术研究所
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