{"title":"David Zhang","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eDavid Zhang \u003c\/strong\u003e(Life Fellow, IEEE) graduated from Peking University, Beijing, China, in 1974 and received the M.S. and first Ph.D. degrees in computer science from the Harbin Institute of Technology, Harbin, China, in 1982 and 1985, respectively. He also got his second Ph.D. degree in electrical and computer engineering from the University of Waterloo, ON, Canada, in 1994. From 1986 to 1988, he was a postdoctoral fellow with Tsinghua University, Beijing, and then an associate professor with the Academia Sinica, Beijing. He has been a chair professor with the Hong Kong Polytechnic University, Hong Kong, where he is the Founding Director of Biometrics Research Centre (UGC\/CRC) supported by the Hong Kong SAR Government since 1998. He is currently a distinguished presidential chair professor with the Chinese University of Hong Kong (Shenzhen), Shenzhen, China. Over the past 40 years, he has been working on pattern recognition, image processing, and biometrics, where many research results have been awarded and some created directions, including medical biometrics and computerized TCM, are famous in the world. He has published 20+ monographs, 500+ international journal papers, and 50+ patents from the USA, Japan, and China. He has been continuously eight years listed as a global highly cited researcher in engineering by Clarivate Analytics. He is also ranked 70th with H-Index 133 at top 1,000 scientists for International Computer Science in 2023. Prof. Zhang has been selected as a fellow of both Royal Society of Canada (RSC) and Canadian Academy of Engineering (CAE). He is also a Croucher senior research fellow, a distinguished speaker of the IEEE Computer Society, an IAPR, and an AAIA fellow.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e Yuan Xie \u003c\/strong\u003ereceived the B.S. degree in Math and Statistics from Xi’an Jiao Tong University, Xi’an, China, in 2022. He is a Ph.D student of Prof. David Zhang and is currently pursuing the Ph.D. degree in School of Data Science from The Chinese University of Hong Kong, Shenzhen, China, under the supervision of Prof. David Zhang. His research interests include pattern recognition, deep learning, computer vision and image processing.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e \u003cstrong\u003eTianhao Peng\u003c\/strong\u003e received the B.S. degree from Changchun Normal University, Changchun, China in 2005, the M.S. degree from Yunnan University, Kunming, China in 2012. He is currently pursuing the Ph.D. degree in computer science and technology from School of Computer Science and Technology, Guizhou University, Guiyang, China. He is also currently an associate professor with the department of automation, Moutai Institute, Renhuai, China. His current research interests include pattern recognition, computer vision, and machine learning.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e \u003cstrong\u003eBaoyuan Wu\u003c\/strong\u003e is an associate professor of School of Data Science, the Chinese University of Hong Kong, Shenzhen (CUHKShenzhen). He is also the director of the Secure Computing Lab of Big Data, Shenzhen Research Institute of Big Data (SBRID). On June 2014, he received the PhD degree from the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. From November 2016 to August 2020, he was a senior and principal researcher at Tencent AI lab. His research interests are AI security and privacy, machine learning, computer vision, and optimization. He has published 40+ top-tier conference and journal papers, including TPAMI, IJCV, NeurIPS, CVPR, ICCV, ECCV, ICLR, and AAAI, and one paper was selected as the Best Paper Finalist of CVPR 2019. He serves as an associate editor of Neurocomputing, area chair of ICLR 2022, AAAI 2022 and ICIG 2021, senior program committee member of AAAI 2021 and IJCAI 2020\/2021, task force member of CCF and CAA. He is the principal investigator of General Program of National Natural Science Foundation of China, 2021 CCF-Tencent Rhino-Bird Young Faculty Open Research Fund, and 2021 Tencent Rhino-Bird Special Research Fund.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e Yuan Xie \u003c\/strong\u003ereceived the B.S. degree in Math and Statistics from Xi’an Jiao Tong University, Xi’an, China, in 2022. He is a Ph.D student of Prof. David Zhang and is currently pursuing the Ph.D. degree in School of Data Science from The Chinese University of Hong Kong, Shenzhen, China, under the supervision of Prof. David Zhang. His research interests include pattern recognition, deep learning, computer vision and image processing.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e \u003cstrong\u003eTianhao Peng\u003c\/strong\u003e received the B.S. degree from Changchun Normal University, Changchun, China in 2005, the M.S. degree from Yunnan University, Kunming, China in 2012. He is currently pursuing the Ph.D. degree in computer science and technology from School of Computer Science and Technology, Guizhou University, Guiyang, China. He is also currently an associate professor with the department of automation, Moutai Institute, Renhuai, China. His current research interests include pattern recognition, computer vision, and machine learning.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e \u003cstrong\u003eBaoyuan Wu\u003c\/strong\u003e is an associate professor of School of Data Science, the Chinese University of Hong Kong, Shenzhen (CUHKShenzhen). He is also the director of the Secure Computing Lab of Big Data, Shenzhen Research Institute of Big Data (SBRID). On June 2014, he received the PhD degree from the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. From November 2016 to August 2020, he was a senior and principal researcher at Tencent AI lab. His research interests are AI security and privacy, machine learning, computer vision, and optimization. He has published 40+ top-tier conference and journal papers, including TPAMI, IJCV, NeurIPS, CVPR, ICCV, ECCV, ICLR, and AAAI, and one paper was selected as the Best Paper Finalist of CVPR 2019. He serves as an associate editor of Neurocomputing, area chair of ICLR 2022, AAAI 2022 and ICIG 2021, senior program committee member of AAAI 2021 and IJCAI 2020\/2021, task force member of CCF and CAA. He is the principal investigator of General Program of National Natural Science Foundation of China, 2021 CCF-Tencent Rhino-Bird Young Faculty Open Research Fund, and 2021 Tencent Rhino-Bird Special Research Fund.\u003c\/p\u003e","products":[{"product_id":"facial-beauty-analysis-david-zhang-ebook","title":"Facial Beauty Analysis","description":"\u003cp\u003eFacial beauty is an intriguing and multifaceted subject that has captivated human interest, crossing cultural and scientific boundaries for centuries. In today’s digital age, understanding facial beauty is no longer just an art but a sophisticated science, the analysis and enhancement of facial beauty leverage advanced technologies such as machine learning, computer vision, and biometrics. This book, “Facial Beauty Analysis: Computational Aesthetics,” is based on our research and aims to offer an in-depth exploration of the latest advancements on both 2D and 3D facial beauty analysis.\u003c\/p\u003e\n\u003cp\u003eBy combining principles from computer vision, pattern recognition, machine learning, and deep learning, this book provides comprehensive insights into landmark detection, feature extraction, beauty prediction, and facial attractiveness enhancement. It introduces cutting-edge innovations such as geometric prior guided hybrid deep neural networks, GAN-based facial beautification, and 3D facial beauty analysis, ensuring readers are equipped with the latest advancements. The content is thoughtfully crafted to empower readers with both foundational concepts and the latest tools required to stay ahead in this rapidly evolving domain.\u003c\/p\u003e\n\u003cp\u003eTargeted toward researchers, professionals, and graduate students, “Facial Beauty Analysis: Computational Aesthetics,” aims to systematically cover both 2D and 3D facial beauty analysis, providing comprehensive insights into feature extraction, beauty prediction, and facial enhancement. This book offers both foundational knowledge and cutting-edge methodologies to advance the field of facial beauty analysis. Whether you’re exploring the fundamentals or seeking to apply the latest technologies, this book is a valuable asset for anyone dedicated to advancing the field of facial beauty analysis.\u003c\/p\u003e","brand":"David Zhang","offers":[{"title":"Default Title","offer_id":53628168372551,"sku":"9789819561445","price":192.59,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0920\/5455\/2903\/files\/facial-beauty-analysis-ebook.webp?v=1775019721"},{"product_id":"advanced-palmprint-authentication-david-zhang-ebook","title":"Advanced Palmprint Authentication","description":"\u003cp\u003eThis book presents a comprehensive exploration of palmprint recognition, synthesizing over a decade of research in contact-based, contactless, 3D, and multispectral systems. As one of the earliest approaches in biometrics, contact-based palmprint systems have evolved significantly, achieving greater portability and accuracy, even when handling large-scale datasets. In contrast, contactless systems, which allow users to position their palms near the camera without physical contact, offer a hygienic, user-friendly alternative that has quickly gained popularity in various applications. Additionally, the advancement of 3D palmprint recognition and the introduction of cutting-edge sensors, such as line-scan and multicamera systems, have further enhanced the accuracy and reliability of these systems.\u003c\/p\u003e\n\n\u003cp\u003eThis book is structured into 13 chapters, divided into three key sections. The first part delves into contact-based systems, emphasizing their growing efficiency and performance in both small devices and large-scale scenarios. The second part provides in-depth coverage of contactless systems, detailing essential processes like palmprint acquisition, ROI localization, feature extraction, and matching techniques. The third section examines the latest developments in multiple sensing systems, focusing on 3D and multispectral recognition.\u003c\/p\u003e\n\n\u003cp\u003eTargeted at researchers and engineers in biometrics, particularly those specializing in palmprint recognition, this book offers valuable insights and practical algorithms for enhancing system performance. It is also an excellent resource for readers with a broader interest in biometric technologies, offering a rich understanding of the latest trends and innovations in the field.\u003c\/p\u003e","brand":"David Zhang","offers":[{"title":"Default Title","offer_id":53653338882375,"sku":"9789819671014","price":203.29,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0920\/5455\/2903\/files\/advanced-palmprint-authentication-ebook-cover.webp?v=1775397744"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0920\/5455\/2903\/collections\/david-zhang-autor-kollektion.webp?v=1775019719","url":"https:\/\/www.cinebuch.de\/collections\/david-zhang.oembed","provider":"CineBuch","version":"1.0","type":"link"}