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Advances in 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, 2023Edition: 2nd ed. 2023.Description: VIII, 111 p. 52 illus., 48 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031329067.Subject(s): Computer vision | Biometric identification | Pattern recognition systems | Image processing -- Digital techniques | Artificial intelligence | Data protection | Computer Vision | Biometrics | Automated Pattern Recognition | Computer Imaging, Vision, Pattern Recognition and Graphics | Artificial Intelligence | Data and Information SecurityAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.37 Online resources: Click here to access online
Contents:
Introduction -- Face Presentation Attack Detection (PAD) Challenges -- Winners' Methods -- Challenge Performances -- Conclusions and Future Works.
In: Springer Nature eBookSummary: This book revises and expands upon the prior edition of Multi-Modal Face Presentation Attack Detection. The authors begin with fundamental and foundational information on face spoofing attack detection, explaining why the computer vision community has intensively studied it for the last decade. The authors also discuss the reasons that cause face anti-spoofing to be essential for preventing security breaches in face recognition systems. In addition, the book describes the factors that make it difficult to design effective methods of face presentation attack detection challenges. The book presents a thorough review and evaluation of current techniques and identifies those that have achieved the highest level of performance in a series of ChaLearn face anti-spoofing challenges at CVPR and ICCV. The authors also highlight directions for future research in face anti-spoofing that would lead to progress in the field. Additional analysis, new methodologies, and a more comprehensive survey of solutions are included in this new edition.
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Introduction -- Face Presentation Attack Detection (PAD) Challenges -- Winners' Methods -- Challenge Performances -- Conclusions and Future Works.

This book revises and expands upon the prior edition of Multi-Modal Face Presentation Attack Detection. The authors begin with fundamental and foundational information on face spoofing attack detection, explaining why the computer vision community has intensively studied it for the last decade. The authors also discuss the reasons that cause face anti-spoofing to be essential for preventing security breaches in face recognition systems. In addition, the book describes the factors that make it difficult to design effective methods of face presentation attack detection challenges. The book presents a thorough review and evaluation of current techniques and identifies those that have achieved the highest level of performance in a series of ChaLearn face anti-spoofing challenges at CVPR and ICCV. The authors also highlight directions for future research in face anti-spoofing that would lead to progress in the field. Additional analysis, new methodologies, and a more comprehensive survey of solutions are included in this new edition.

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