Multi-Modal Face Presentation Attack Detection


Multi-Modal Face Presentation Attack Detection
by 作者: Jun Wan
Published: 2020
ISBN-13 书号: 9781681739243
Pages: 88
Language 语言: English
Pages: PDF
Size: 10 Mb
ISBN 1681739240


Book Description
Multi-Modal Face Presentation Attack Detection (Synthesis Lectures on Computer Vision)
For the last ten years,face biometric research has been intensively studied by 作者: 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 of the novel methodologies proposed in the context of the challenge achieved state-of-the-art performance. In this manuscript,we provide a comprehensive review on face anti-spoofing techniques presented in this joint event and point out directions for future research on the face anti-spoofing field.

下载地址:

Multi-Modal Face Presentation Attack Detection 9781681739243.pdf

打赏
未经允许不得转载:finelybook » Multi-Modal Face Presentation Attack Detection

相关推荐

  • 暂无文章

评论 抢沙发

  • 昵称 (必填)
  • 邮箱 (必填)
  • 网址

觉得文章有用就打赏一下

您的打赏,我们将继续给力更多优质内容

支付宝扫一扫打赏

微信扫一扫打赏