Multi-Modal Face Presentation Attack Detection
(Sprache: Englisch)
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...
Voraussichtlich lieferbar in 3 Tag(en)
versandkostenfrei
Buch (Kartoniert)
36.29 €
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenlose Rücksendung
Produktdetails
Produktinformationen zu „Multi-Modal Face Presentation Attack Detection “
Klappentext zu „Multi-Modal Face Presentation Attack Detection “
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.
Inhaltsverzeichnis zu „Multi-Modal Face Presentation Attack Detection “
Preface.- Acknowledgments.- Motivation and Background.- Multi-Modal Face Anti-Spoofing Challenge.- Review of Participants' Methods.- Challenge Results.- Conclusions and Future Works.- Bibliography.- Authors' Biographies.
Autoren-Porträt von Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, Stan Z. Li
Jun Wan received a B.S. degree from the China University of Geosciences, Beijing, China, in 2008, and a Ph.D. degree from the Institute of Information Science, Beijing Jiaotong University, Beijing, China, in 2015. Since January 2015, he has been a Faculty Member with the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Science (CASIA), China, where he currently serves as an Associate Professor. He is an IEEE Senior Member and a director of Chalearn Challenges. He has published more than 50 research papers and has been guest editor at TPAMI, MVA, and Entropy. His main research interests include computer vision, machine learning, especially for face and pedestrian analysis (such as attribute analysis, face anti-spoofing detection), gesture and sign language recognition. He has published papers in top journals and conferences, such as JMLR, T-PAMI, T-IP, T-MM, T-CYB, TOMM, PR, CVIU, CVPR, AAAI, and IJCAI. He has served as the reviewer on several journals and conferences, such as JMLR, T-PAMI, T-IP, T-MM, T-SMC, PR, CVPR, ICCV, ECCV, AAAI, and ICRA.Guodong Guo received a B.E. degree in automation from Tsinghua University, Beijing, China, and a Ph.D. degree in computer science from University of Wisconsin, Madison, WI. He is currently the Deputy Head of the Institute of Deep Learning, Baidu Research, and also an Associate Professor with the Department of Computer Science and Electrical Engineering, West Virginia University (WVU). In the past, he visited and worked in several places, including INRIA, Sophia Antipolis, France; Ritsumeikan University, Kyoto, Japan; and Microsoft Research, Beijing, China; He authored a book, Face, Expression, and Iris Recognition Using Learning-based Approaches (2008), co-edited two books, Support Vector Machines Applications (2014) and Mobile Biometrics (2017), and published over 100 technical papers. He is an Associate Editor of IEEE Transactions on Affective Computing, Journal of Visual
... mehr
Communication and Image Representation, and serves on the editorial board of IET Biometrics. His research interests include computer vision, biometrics, machine learning, and multimedia. He received the North Carolina State Award for Excellence in Innovation in 2008, Outstanding Researcher (2017-2018, 2013-2014) at CEMR, WVU, and New Researcher of the Year (2010-2011) at CEMR, WVU. He was selected the "People's Hero of the Week" by BSJB under Minority Media and Telecommunications Council (MMTC) in 2013. Two of his papers were selected as "The Best of FG'13" and "The Best of FG'15", respectively.Sergio Escalera (www.sergioescalera.com) obtained a P.h.D. degree on multi-class visual categorization systems at Computer Vision Center, UAB. He obtained the 2008 best Thesis award on Computer Science at Universitat Autonoma de Barcelona. He is ICREA Academia. He leads the Human Pose Recovery and Behavior Analysis Group at UB, CVC, and the Barcelona Graduate School of Mathematics. Heis Full Professor at the Department of Mathematics and Informatics, Universitat de Barcelona. He is an adjunct professor at Universitat Oberta de Catalunya, Aalborg University, and Dalhousie University. He has been visiting professor at TU Delft and Aalborg Universities. He is also a member of the Computer Vision Center at UAB. He is series editor of The Springer Series on Challenges in Machine Learning. He is a member and fellow of the European Laboratory of Intelligent Systems ELLIS. He is vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is a co-creator of Codalab open source platform for challenges organization. He is Chair of IAPR TC-12: Multimedia and visual information systems. His research interests include: statistical pattern recognition, affective computing, and human pose recovery and behavior understanding, including multi-modal data analysis, with special interest in characterizing people: personality and psychological
... weniger
Bibliographische Angaben
- Autoren: Jun Wan , Guodong Guo , Sergio Escalera , Hugo Jair Escalante , Stan Z. Li
- 2020, XI, 76 Seiten, Maße: 19,1 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3031006968
- ISBN-13: 9783031006968
Sprache:
Englisch
Kommentar zu "Multi-Modal Face Presentation Attack Detection"
Schreiben Sie einen Kommentar zu "Multi-Modal Face Presentation Attack Detection".
Kommentar verfassen