This book highlights the field of selfie biometrics, providing a clear overview and presenting recent advances and challenges. It also discusses numerous selfie authentication techniques on mobile devices. Biometric authentication using mobile devices is becoming a convenient and important means of verifying identity for secured access and services such as telebanking and electronic transactions. In this context, face and ocular biometrics in the visible spectrum has gained increased attention from the research community.

However, device mobility and operation in uncontrolled environments mean that facial and ocular images captured with mobile devices exhibit substantial degradation as a result of adverse lighting conditions, specular reflections and motion and defocus blur. In addition, low spatial resolution and the small sensor of front-facing mobile cameras further degrade the sample quality, reducing the recognition accuracy of face and ocular recognition technology when integrated into smartphones.

Presenting the state of the art in mobile biometric research and technology, and offering an overview of the potential problems in real-time integration of biometrics in mobile devices, this book is a valuable resource for final-year undergraduate students, postgraduate students, engineers, researchers and academics in various fields of computer engineering.

Presents the first comprehensive book on state-of-the-art methods for selfie face, finger and ocular biometrics for mobile user authentication Reviews the latest developments on privacy, security, usability, liveness detection and soft-biometrics prediction from selfie images Enlists the challenges involved in real-time integration of selfie biometrics for mobile use cases Provides expert insights from a range of disciplines, including computational intelligence, mobile computing, advanced machine learning, adversarial pattern classification, and signal processing

Autorentext

Ajita Rattani is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Wichita State University since 2019. Prior to this, she was an Adjunct Graduate Faculty at University of Missouri- Kansas City. She did her Post-doctoral and PhD. studies from Michigan State University and University of Cagliari, Italy, respectively. Her field of research is Biometrics, Machine Learning, Deep Learning, Image Processing and Computer Vision. She is the co-editor of the Springer book titled Adaptive Biometric Systems: Recent Advances and Challenges. She has received number of best paper awards at IEEE international conferences and is an editorial board member of IEEE Biometrics Council.

Reza Derakhshani is an Associate Professor of Computer Science and Electrical Engineering at University of Missouri, Kansas City. He is also the Chief Scientist and technology inventor at EyeVerify (now ZOLOZ), a Kansas City biometricstartup that was acquired by Alibaba's Ant Financial in 2016. He earned his Ph.D. and Master's degrees in Computer and Electrical Engineering from West Virginia University. Dr. Derakhshani's research interests are in biometrics, computational imaging, and biomedical signal and image processing using computational intelligence paradigms. His work has been sponsored by private industry and various state and federal agencies, and has resulted in many publications and issued U.S. and international patents.

Arun Ross is a Professor in the Department of Computer Science and Engineering at Michigan State University. Prior to joining MSU, he was a faculty member at West Virginia University. He is the coauthor of the books Introduction to Biometrics and Handbook of Multibiometrics. He is a recipient of the JK Aggarwal Prize and the Young Biometrics Investigator Award from the International Association of Pattern Recognition for his contributions to the field of Pattern Recognition and Biometrics. He was designated a Kavli Fellow by the US National Academy of Sciences by virtue of his presentation of the 2006 Kavli Frontiers of Sciences Symposium.

Inhalt

1. Introduction to Selfie Biometrics.- Part I: Selfie Finger, Ocular and Face Biometrics .- 2. User Authentication via Finger-selfies.- 3. A Scheme for Fingerphoto Recognition in Smartphones.- 4. MICHE Competitions: A Realistic Experience with Uncontrolled Eye Region Acquisition.- 5. Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris.- 6. Foveated Vision for Biologically-inspired Continuous face Authentication.