Mobile biometrics - the use of physical and/or behavioral characteristics of humans to allow their recognition by mobile/smart phones - aims to achieve conventional functionality and robustness while also supporting portability and mobility, bringing greater convenience and opportunity for its deployment in a wide range of operational environments from consumer applications to law enforcement. But achieving these aims brings new challenges such as issues with power consumption, algorithm complexity, device memory limitations, frequent changes in operational environment, security, durability, reliability, and connectivity. Mobile Biometrics provides a timely survey of the state of the art research and developments in this rapidly growing area.
Topics covered in Mobile Biometrics include mobile biometric sensor design, deep neural network for mobile person recognition with audio-visual signals, active authentication using facial attributes, fusion of shape and texture features for lip biometry in mobile devices, mobile device usage data as behavioral biometrics, continuous mobile authentication using user phone interaction, smartwatch-based gait biometrics, mobile four-fingers biometrics system, palm print recognition on mobile devices, periocular region for smartphone biometrics, and face anti-spoofing on mobile devices.
- Chapter 2: Mobile biometric device design: history and challenges
- Chapter 3: Challenges in developing mass-market mobile biometric sensors
- Chapter 4: Deep neural networks for mobile person recognition with audio-visual signals
- Chapter 5: Active authentication using facial attributes
- Chapter 6: Fusion of shape and texture features for lip biometry in mobile devices
- Chapter 7: Mobile device usage data as behavioral biometrics
- Chapter 8: Continuous mobile authentication using user-phone interaction
- Chapter 9: Smartwatch-based gait biometrics
- Chapter 10: Toward practical mobile gait biometrics
- Chapter 11: 4F™-ID: mobile four-fingers biometrics system
- Chapter 12: Palmprint recognition on mobile devices
- Chapter 13: Addressing the presentation attacks using periocular region for smartphone biometrics
- Chapter 14: Countermeasures to face photo spoofing attacks by exploiting structure and texture information from rotated face sequences
- Chapter 15: Biometric antispoofing on mobile devices
- Chapter 16: Biometric open protocol standard
- Chapter 17: Big data and cloud identity service for mobile authentication
- Chapter 18: Outlook for mobile biometrics
West Virginia University, Department of Computer Science and Electrical Engineering, USA.
Dr. Guodong Guo is Associate Professor in the Department of Computer Science and Electrical Engineering at West Virginia University, USA. He is also the Director and Founder of the Computer Vision Laboratory (CVL) at WVU, and affiliated with the Center for Identification Technology Research (CITeR), a unique national Biometric Research Center funded by the NSF.Harry Wechsler Professor of Computer Science.
George Mason University, Department of Computer Science, USA.
Dr. Harry Wechsler is Professor of Computer Science at George Mason University (GMU) in Fairfax, VA, USA. He has been active in biometrics, face recognition, forensics and evidence analysis, smart identity and information management, contents-based image retrieval (CBIR), and cyber security, biomedical image processing, image analysis and understanding, data mining, machine learning and pattern recognition, with research funding from ARL, DARPA, DOD / TSWG, FBI, and NSF. He is an IEEE Fellow and an IAPR Fellow.