Ordinal regression with representative feature strengthening for face anti-spoofing
文献类型：期刊论文
| 作者 | Jiang, Fangling1,2,3; Liu, Pengcheng2,3 ; Zhou, Xiang-Dong2,3
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| 刊名 | NEURAL COMPUTING & APPLICATIONS
 |
| 出版日期 | 2022-05-02 |
| 页码 | 17 |
| 关键词 | Face anti-spoofing Ordinal regression Representative feature strengthening Inter-class relationships |
| ISSN号 | 0941-0643 |
| DOI | 10.1007/s00521-022-07272-8 |
| 通讯作者 | Jiang, Fangling(jfl@usc.edu.cn) |
| 英文摘要 | Face anti-spoofing is a crucial link to ensure the security of face recognition. This paper proposes a novel face anti-spoofing method, which performs ordinal regression with representative feature strengthening to learn generalized and discriminative representation for the live and spoof face classification. Specifically, we propose a semantic label schema, which encodes the inter-class ordinal relationships among live and various spoof faces into supervision information to supervise deep neural networks to perform ordinal regression. It enables the learned model to finely constrain the relative distances among features of different categories in the feature space according to the ordinal relationships. The representative feature strengthening network is designed to strengthen important features and meanwhile weaken redundant features for the classification decision. It leverages a dual-task architecture that takes the same single image as input and shares representations via feature fusing blocks. The network first fuses hierarchical paired convolutional features of two streams to learn the common concern of the two related tasks and then, aggregates the learned local convolutional features into a global representation by a learnable feature weighting block. The network is trained to minimize the Kullback-Leibler divergence loss in an end-to-end manner supervised by the semantic labels. Extensive intra-dataset and cross-dataset experiments demonstrate that the proposed method outperforms the state-of-the-art approaches on four widely used face anti-spoofing datasets. |
| 资助项目 | National Natural Science Foundation of China[61806185] ; National Natural Science Foundation of China[61802361] ; Technology Innovation and Application Development Project in Chongqing[cstc2019jscx-msxmX0299] ; Technology Innovation and Application Development Project in Chongqing[cstc2019jscx-gksbX0073] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000789750600001 |
| 出版者 | SPRINGER LONDON LTD |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/15861]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Jiang, Fangling |
| 作者单位 | 1.Univ South China, Sch Comp Sci, 28 Changsheng West Rd, Hengyang 421001, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, 266 Fangzheng Ave, Chongqing 400714, Peoples R China 3.Univ Chinese Acad Sci, 19 Yuquan Rd, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Fangling,Liu, Pengcheng,Zhou, Xiang-Dong. Ordinal regression with representative feature strengthening for face anti-spoofing[J]. NEURAL COMPUTING & APPLICATIONS,2022:17. |
| APA | Jiang, Fangling,Liu, Pengcheng,&Zhou, Xiang-Dong.(2022).Ordinal regression with representative feature strengthening for face anti-spoofing.NEURAL COMPUTING & APPLICATIONS,17. |
| MLA | Jiang, Fangling,et al."Ordinal regression with representative feature strengthening for face anti-spoofing".NEURAL COMPUTING & APPLICATIONS (2022):17. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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