Multi-Modal Face Presentation Attack Detection / by Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, Stan Z. Li.

Format:

Book

Author/Creator:

Wan, Jun., Author.

Guo, Guodong., Author.

Escalera, Sergio., Author.

Escalante, Hugo Jair., Author.

Li, Stan Z., Author.

Series:

Synthesis Lectures on Computer Vision, 2153-1064

Language:

English

Subjects (All):

Image processing—Digital techniques.

Computer vision.

Pattern recognition systems.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computer Vision.

Automated Pattern Recognition.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computer Vision.

Automated Pattern Recognition.

Physical Description:

1 online resource (XI, 76 p.)

Edition:

1st ed. 2020.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

Summary:

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.

Contents:

Preface

Acknowledgments

Motivation and Background

Multi-Modal Face Anti-Spoofing Challenge

Review of Participants' Methods

Challenge Results

Conclusions and Future Works

Bibliography

Authors' Biographies.

ISBN:

9783031018244

3031018249