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Multi-modal face presentation attack detection / Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, Stan Z. Li.

By: Wan, Jun (Ph. D) [author.]Contributor(s): Guo, Guodong [author.] | Escalera, Sergio [author.] | Escalante, Hugo Jair [author.] | Li, S. Z, 1958- [author.]Material type: Text

TextSeries: Synthesis digital library of engineering and computer science | Synthesis lectures on computer vision ; #17.Publisher: San Rafael, California (1537 Fourth Street, 1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, [2020]Description: 1 PDF (xi, 76 pages) : illustrations (some color)Content type: text Media type: electronic Carrier type: online resourceISBN: 9781681739236Subject(s): Human face recognition (Computer science) | Computer security | Computer crimes -- Prevention | face presentation attack detection | face anti-spoofing | multi-modal data analysisGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 006.4 LOC classification: TA1653 | .W366 2020ebOnline resources: Abstract with links to full text | Abstract with links to resource Also available in print.

Contents:

1. Motivation and background -- 1.1. Introduction -- 1.2. Background

2. Multi-modal face anti-spoofing challenge -- 2.1. CASIA-SURF dataset -- 2.2. Challenge based on the CASIA-SURF dataset -- 2.3. Dataset application

3. Review of participants' methods -- 3.1. Baseline method -- 3.2. Participants' methods

4. Challenge results -- 4.1. Experiments -- 4.2. Summary

5. Conclusions and future works -- 5.1. Conclusions -- 5.2. Future work.

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.

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Item type	Current library	Call number	Status	Date due	Barcode
Ebooks	Indian Institute of Technology Delhi - Central Library		Available

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references.

1. Motivation and background -- 1.1. Introduction -- 1.2. Background

2. Multi-modal face anti-spoofing challenge -- 2.1. CASIA-SURF dataset -- 2.2. Challenge based on the CASIA-SURF dataset -- 2.3. Dataset application

3. Review of participants' methods -- 3.1. Baseline method -- 3.2. Participants' methods

4. Challenge results -- 4.1. Experiments -- 4.2. Summary

5. Conclusions and future works -- 5.1. Conclusions -- 5.2. Future work.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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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.

Also available in print.

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