It takes the average reader 1 hour and 30 minutes to read Multi-Modal Face Presentation Attack Detection by Jun Wan
Assuming a reading speed of 250 words per minute. Learn more
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 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.
Multi-Modal Face Presentation Attack Detection by Jun Wan is 90 pages long, and a total of 22,500 words.
This makes it 30% the length of the average book. It also has 27% more words than the average book.
The average oral reading speed is 183 words per minute. This means it takes 2 hours and 2 minutes to read Multi-Modal Face Presentation Attack Detection aloud.
Multi-Modal Face Presentation Attack Detection is suitable for students ages 10 and up.
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