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.

Identifies challenges when designing and implementing methods of face spoofing attack detection Explains why face anti-spoofing is essential for preventing security breaches in face recognition systems Provides current methods of face anti-spoofing and highlights directions for future research in the field

Autorentext

Jun Wan (IEEE Senior Member), Ph.D., is an Associate Professor at the Chinese Academy of Sciences (CASIA) Institute of Automation, where he has been a faculty member since 2015. Dr. Wan earned his Ph.D. from Beijing Jiaotong University in 2015. He served as the Co-Editor for special issues in the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), International Journal of Computer Vision (IJCV), and the IEEE Transactions on Biometrics, Behavior, and Identity Science (TBIOM). He is also an Associate Editor of IET Biometrics. He has been involved in the organization of several face anti-spoofing challenges in computer vision collocated with top venues. He has served as competition chair of CVPR2019, CVPR2020, ICCV2021, and CVPR2023. His research interests include computer vision including face analysis, gesture recognition, and sign language translation. Guodong Guo, Ph.D., is affiliated with West Virginia University. He earned his Ph.D. in computer science from the University of Wisconsin, Madison. Previously, he was the Head of the Institute of Deep Learning at Baidu Research. He has written and edited four books and published over 200 technical papers. Dr. Guo is an Associate Editor of IEEE Transactions on Affective Computing, Journal of Visual Communication and Image Representation, and serves on the editorial board of IET Biometrics. His research interests include computer vision, biometrics, machine learning, and multimedia. Sergio Escalera, Ph.D., is a Full Professor with the Department of Mathematics and Informatics at Universitat de Barcelona. He earned his Ph.D. in multiclass visual categorization systems from the Computer Vision Center, UAB, where he is still a member. In addition, he leads the Human Pose Recovery and Behavior Analysis Group and is a Distinguished Professor with Aalborg University. Dr. Escalera serves as the Vice-President of ChaLearn Challenges in Machine Learning and as the chair of IAPR TC-12: Multimedia and Visual Information Systems. He co-created the Codalab open-source platform for challenges organization. He is also a Series Editor of The Springer Series on Challenges in Machine Learning. His research interests include automatic analysis of humans from visual and multimodal data, with special interest in inclusive, transparent, and fair affective computing and people characterization. Hugo Jair Escalante is a Senior Researcher Scientist at INAOE, Mexico, a membof the board of directors of ChaLearn USA, and Chair officer of the IAPR Technical Committee 12. er He is a regular member of the Mexican Academy of Sciences (AMC), the Mexican Academy of Computing (AMEXCOMP) and Mexican System of Researchers Level II (SNI). He was editor of the Springer Series on Challenges in Machine Learning 2017-2013 and is Associate Editor of IEEE Transactions on Affective Computing. He has been involved in the organization of several challenges in machine learning and computer vision collocated with top venues. He has served as competition chair of NeurIPS2020, FG2020 and ICPR2020, NeurIPS2019, PAKDD2019-2018, IJCNN2019. His research interests are on machine learning, challenge organization, and its applications on language and vision. Stan Z. Li (IEEE Fellow, IAPR Fellow) is a Chair Professor of artificial intelligence at Westlake University. He received his Ph.D. degree from Surrey University, UK, in 1991. He was awarded Honorary Doctorate of Oulu University, Finland, in 2013. He was the director of the Center for Biometrics and Security Research (CBSR) , Chinese Academy of Sciences, 2004~2019. He worked at Microsoft Research Asia as a Research Lead, 2000~2004. Prior to that, he was an associate professor (tenure) at Nanyang Technological University, Singapore. He joined Westlake University as a Chair Professor of Artificial Intelligence in February 2019. Stan Z. Li has publ

Klappentext

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.

Inhalt
Introduction.- Face Presentation Attack Detection (PAD) Challenges.- Winners' Methods.- Challenge Performances.- Conclusions and Future Works