Advances in Face Presentation Attack Detection

Descripción:

: Esta obra revisa y amplía la edición anterior sobre la detección de ataques de presentación facial multimodal. Los autores proporcionan información fundamental sobre la detección de suplantación de identidad (spoofing), explicando la importancia de estas técnicas para prevenir brechas de seguridad en los sistemas de reconocimiento facial. El libro analiza los factores que dificultan el diseño de métodos efectivos y presenta una revisión exhaustiva de las técnicas actuales que han logrado el mayor rendimiento en desafíos internacionales como ChaLearn en CVPR e ICCV.Además de evaluar las metodologías existentes, el texto identifica direcciones futuras para la investigación en el campo de la biometría. Esta segunda edición incluye análisis adicionales, nuevas metodologías y una encuesta más completa de soluciones, siendo una lectura esencial para profesionales y académicos interesados en la visión por computadora y la seguridad de datos. EAN: 9783031329081 Tipo: Libros Categoría: Tecnología|Ciencias Título: Advances in Face Presentation Attack Detection Autor: Jun Wan| Guodong Guo| Sergio Escalera| Hugo Jair Escalante| Stan Z. Li Editorial: Springer-Verlag GmbH Idioma: en Páginas: 119 Formato: tapa blanda. N° de ref. del artículo Happ-2026-04-28-abe87f13

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Acerca del autor: 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 published over 400 papers in international journals and conferences, authored, and edited 10 books, with over 60,000 Google Scholar citations. Among these are Markov Random Field Models in Image Analysis (Springer), Handbook of Face Recognition (Springer) and Encyclopedia of Biometrics (Springer). He served as an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence and organized more than 100 international conferences or workshops. His current research interests include AI fundamental research and AI for sciences.