Guest Editorial Special Issue on Mobile Biometrics

Abstract

Biometrics involves the use of physical and/or behavioural traits for human authentication and identification. It is a well-established and growing discipline supporting many practical applications including cyber security and mobile devices. In recent years, stimulated by hardware advances and a rapidly growing consumer market built around increasingly powerful mobile phones and other portable platforms, mobile biometrics has become an important challenge for biometric applications and will continue to do so. It is predicted that the global market for mobile biometrics will grow substantially in the coming years. Mobile biometrics aims to achieve the functionality and robustness of conventional biometrics while also supporting portability and mobility in order to bring greater convenience, flexibility, and opportunity for its deployment in a wide range of operational environments ranging from consumer applications to law enforcement. However, achieving these aims brings new challenges, such as issues about power consumption, algorithmic complexity, device memory limitations, frequent changes in operational environment, security, durability, reliability, connectivity, and so on. This Special Issue (SI) aims to bring together some good examples of current research that addresses some of the major challenges for mobile biometrics. The SI provides a platform for both academic researchers and industry partners to become familiar with the latest new research, stimulating discussion on existing and emerging challenges in mobile biometrics, and raising awareness of potential solutions to advance research in mobile biometrics. After careful review and selection, this SI has accepted four papers from those submitted. These four papers cover mobile biometrics from different aspects, which is detailed in the following. The first paper, ‘A Method for Using Visible Ocular Vasculature for Mobile Biometrics’, by V. Gottemukkula, S. Saripalle, S. Tankasala, and R. Derakhshani, investigates the use of existing visible light cameras in mobile phones for vascular pattern segmentation, extraction and recognition. Several stages are involved in the biometric process, including interest point detection, feature description, and matching, with Gabor phase filters used for feature extraction and matching. Low error rates are reported in the experimental validation of the approach. The study reported shows the feasibility and utility of extracting vascular patterns for user recognition in mobile devices. The second paper, ‘An Authentic Mobile-Biometric Signature Verification System’, by F. Zareen and S. Jabin, presents an overview of typical mobile biometric systems, including different devices and hardware to support mobile biometrics. The paper discusses open issues and challenges for developing mobile biometric systems. To that end the paper describes a mobile biometric system for signature verification, which is supported by experimental validation. The third paper, ‘The Blind Subjects Face Database (BSFD)’, by N. Poh, R. Blanco-Gonzalo, R. Wong and R. Sanchez-Reillo, introduces a new face database captured from blind subjects. The underlying application scenario is to use the face characteristic to unlock a mobile device, which is convenient and fast for real applications. However, one needs to address how well this technique can be used for visually impaired people. The collected database, BSFD, facilitates the usability and face recognition studies involving people with varying degrees of visual impairment for mobile applications. More importantly, the database is publicly available for use by other researchers to investigate related issues further. The last paper, ‘Small Fingerprint Scanners Used in Mobile Devices, The Impact on Biometric Performance’, by B. Fernandez-Saavedra, R. Sanchez-Reillo, R. Ros-Gomez, and J. Liu-Jimenez, examines the performance of fingerprint recognition in mobile devices where the embedded fingerprint scanners could have different sensing areas, capturing fingerprint images of different image quality. Both public and commercial algorithms (including a database specifically assembled for this study) are used for the testing and performance evaluation. Experimental results show that the fingerprint matching performance is affected by the scanner sizes, which are correlated to the fingerprint image quality. All of the papers selected for this SI show that there are new challenges in developing mobile biometric systems as compared to traditional biometrics. It is our hope that this SI will promote and encourage further R&D to address the challenging issues involved in the emerging but rapidly growing field of mobile biometrics. Guodong Guo, Lead Guest Editor, is with the Lane Department of Computer Science and Electrical Engineering, West Virginia University. He is also affiliated with the Center for Identification Technology Research (CITeR), funded by the National Science Foundation (NSF). His research interests include Biometrics and Computer Vision. He authored a book, ‘Face, Expression, and Iris Recognition Using Learning-based Approaches’ (2008), co-edited a book, ‘Support Vector Machines Applications’ (2014), and published over 80 technical papers. He received the North Carolina State Award for Excellence in Innovation in 2008, Outstanding Researcher (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. Harry Wechsler, Guest Editor, serves as Professor of Computer Science at George Mason University (GMU). He has been active in data mining, image analysis, machine learning and pattern recognition, in general, and biometrics, cyber security, face recognition, and performance evaluation (including error analysis), in particular, with research funding coming from ARL, DARPA, DOD/TSWG, and FBI. His R&D focus has been on open (rather than closed) authentication systems (for imposter detection) and re-identification; occluded and corrupt imagery (e.g., denial and deception); interoperability, and uncertainty (including evidence-based reasoning) and uncontrolled settings. He organised and directed the NATO Advanced Study Institute (ASI) on ‘Face Recognition: From Theory to Applications,’ was the principal co – editor for its seminal proceedings published by Springer, and his book on Reliable Face Recognition Methods, which breaks new ground in applied modern pattern recognition and biometrics, was published by Springer in 2007. Dr. Wechsler directed at GMU the design and development of FERET, which has been the major database used for benchmark studies in face recognition. He authored 3 books, published over 300 scientific papers, and has 7 patents (together with his doctoral students). He is a Fellow of IEEE and IAPR (Int. Assoc. for Pattern Recognition). Shiguang Shan, Guest Editor, received Ph.D. degree in computer science from the Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS) in 2004. He has been a Professor of ICT since 2010, and now the Deputy Director of the Key Lab of Intelligent Information Processing of CAS. His research interests cover computer vision, pattern recognition, and machine learning. He especially focuses on face recognition related research topics. He has published more than 200 papers in refereed journals and proceedings in the areas of Computer Vision and Pattern Recognition. He serves as Area Chair at many international conferences including ICCV’11. He is one of the Associate Editors of IEEE Trans. on Image Processing, etc. He received the China's State Scientific and Technological Progress Award in 2005 for his work on face recognition technologies and the China's Natural Science Award in 2015 for his work on non-linear feature extraction. Norman Poh, Guest Editor, joined the Department of Computer Science, University of Surrey in August 2012 as a Lecturer. He received the Ph.D. degree in computer science in 2006 from the Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland. Prior to the current appointment, he was a Research Fellow with the Centre for Vision, Speech, and Signal Processing (CVSSP) and a research assistant at IDIAP research institute. His research objective is to advance pattern recognition techniques with applications to biometrics and healthcare informatics. In these two areas, he has published more than 90 publications, which also include five award-winning papers (AVBPA05, ICB09, HSI 2010, ICPR 2010 and Pattern Recognition Journal 2006). He was the recipient of Swiss NSF Young Prospective and Advance Researcher Fellowships and was given the title of Researcher of the Year 2011, University of Surrey. He currently holds an MRC New Investigator Research Grant (UK) to work on modelling healthcare with applications to chronic kidney disease.