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

By: Wan, Jun [author.].
Contributor(s): Guo, Guodong [author.] | Escalera, Sergio [author.] | Escalante, Hugo Jair [author.] | Li, Stan Z [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Computer Vision: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XI, 76 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031018244.Subject(s): Image processing -- Digital techniques | Computer vision | Pattern recognition systems | Computer Imaging, Vision, Pattern Recognition and Graphics | Computer Vision | Automated Pattern RecognitionAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006 Online resources: Click here to access online
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.
In: Springer Nature eBookSummary: 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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Preface -- Acknowledgments -- Motivation and Background -- Multi-Modal Face Anti-Spoofing Challenge -- Review of Participants' Methods -- Challenge Results -- Conclusions and Future Works -- Bibliography -- Authors' Biographies.

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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