| 書(shū)目名稱 | Multi-Modal Face Presentation Attack Detection | | 編輯 | Jun Wan,Guodong Guo,Stan Z. Li | | 視頻video | http://file.papertrans.cn/640/639977/639977.mp4 | | 叢書(shū)名稱 | Synthesis Lectures on Computer Vision | | 圖書(shū)封面 |  | | 描述 | .For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain...In order to attract researchers to this important field and push the boundaries of the state of the art on face anti-spoofing detection, we organized the Face Spoofing Attack Workshop and Competition at CVPR 2019, an event part of the ChaLearn Looking at People Series. As part of this event, we released the largest multi-modal face anti-spoofing dataset so far, the CASIA-SURF benchmark. The workshop reunited many researchers from around the world and the challenge attracted more than 300 teams. Some o | | 出版日期 | Book 2020 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01824-4 | | isbn_softcover | 978-3-031-00696-8 | | isbn_ebook | 978-3-031-01824-4Series ISSN 2153-1056 Series E-ISSN 2153-1064 | | issn_series | 2153-1056 | | copyright | Springer Nature Switzerland AG 2020 |
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