Advanced Palmprint Authentication
Description
This 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.
This 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.
Targeted 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.
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David Zhang (Life Fellow, IEEE) graduated from Peking University, Beijing, China, in 1974, and received the first Ph.D. degree in computer science from Harbin Institute of Technology, Harbin, China, in 1985, and the second Ph.D. degree in electrical and computer engineering from the University of Waterloo, Waterloo, ON, Canada, in 1994. He has been a Chair Professor at The Hong Kong Polytechnic University, Hong Kong, where he was the Founding Director of the Biometrics Research Centre (UGC/CRC), supported by the Hong Kong SAR Government since 1998.
He is currently a Distinguished Presidential Chair Professor at The Chinese University of Hong Kong (CUHK-Shenzhen), Shenzhen, China. He has been working on pattern recognition, image processing, and biometrics, creating various famous directions, including medical biometrics and computerized TCM.
Prof. Zhang has been selected as a fellow of the Royal Society of Canada (RSC) and the Canadian Academy of Engineering (CAE). He is also a Croucher Senior Research Fellow, a Distinguished Speaker of the IEEE Computer Society, and an IAPR and AAIA Fellow. He has been listed as a Global Highly Cited Researcher in Engineering by Clarivate Analytics for eight years. He is also ranked 70th with H-Index 133 in the Top 1000 Scientists for International Computer Science in 2023.
Dandan Fan received the B.S. degree in mechanical design and manufacturing and automation from Southwest Jiaotong University, Cheng Du, in 2013, the M.S. degree in software engineering from Xi'an Jiaotong University, Xi'an, in 2019. She is currently pursuing the Ph.D. degree with School of Data Science, the Chinese University of Hong Kong (Shenzhen), Shenzhen. Her current research interests include biometrics and computer vision.
Xu Liang received the B.S. degree in communication engineering from China University of Geosciences, Wu Han, in 2012, the M.S. and Ph.D. degrees in computer science and technology from Harbin Institute of Technology, Shenzhen, China, in 2016 and 2023, respectively. From 2016 to 2017, he was a Research Assistant with the Biometrics Research Centre, Hong Kong Polytechnic University. He is currently an Associate Professor with the School of Software, Northwestern Polytechnical University, Xi'an, China. His research interests include biometrics and computer vision.
Bob Zhang (Senior Member, IEEE) received the Ph.D. degree in electrical and computer engineering from the University of Waterloo, Waterloo, ON, Canada, in 2011. After graduating from the University of Waterloo, he remained with the Center for Pattern Recognition and Machine Intelligence, and later he was a Postdoctoral Researcher with the Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. He is currently an Associate Professor with the Department of Computer and Information Science, University of Macau, Macau. His research interests include biometrics, pattern recognition, and image processing. He is a Technical Committee Member of the IEEE Systems, Man, and Cybernetics Society and Associate Editors of IEEE Transactions on Image Processing, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Neural Networks and Learning Systems, and Artificial Intelligence Review.
Content
Chapter 1 Towards Next-Generation Palmprint Recognition.- Part I CONTACT-BASED PALMPRINT RECOGNITION.- Chapter 2 Jointly Heterogeneous Palmprint Discriminant Feature Learning.- Chapter 3 Rich Orientation Coding for Large-Scale Palmprint Image Analysis.- Chapter 4 Hybrid Fusion Combining Palmprint and Palm Vein for Large-scale Palm-based Recognition.- Part II CONTACTLESS PALMPRINT RECOGNITION.- Chapter 5 Keypoint Localization Neural Network for Touchless Palmprint Recognition Based on Edge-Aware Regression.- Chapter 6 Hand-Geometry Aware Image Quality Assessment Framework for Contactless Palmprint Recognition.- Chapter 7 Touchless Palmprint Recognition Based on 3D Gabor Template and Block Feature Refinement.- Chapter 8 Aligned Multilevel Gabor Convolution Network for Palmprint Recognition.- Chapter 9 Contactless Palmprint Recognition System based on Dual-camera Alignment.- Part III MULTIPLE PALMPRINT SENSING SYSTEMS.- Chapter 10 Multi-camera System for High Speed Touchless Palm Recognition.- Chapter 11 Line-Scan Palmprint Acquisition System.- Chapter 12 Person Recognition Using 3-D Palmprint Data Based on Full-Field Sinusoidal FringeProjection.- Chapter 13 Complete Binary Representation for 3-D Palmprint Recognition.- Chapter 14 Book Reivew and Future Work.
