Guodong Guo – författare

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.

For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain.In order to attract researchers to this important field and push the boundaries of the state of the art on face anti-spoofing detection, we organized the Face Spoofing Attack Workshop and Competition at CVPR 2019, an event part of the ChaLearn Looking at People Series. As part of this event, we released the largest multi-modal face anti-spoofing dataset so far, the CASIA-SURF benchmark. The workshop reunited many researchers from around the world and the challenge attracted more than 300 teams. Some of the novel methodologies proposed in the context of the challenge achieved state-of-the-art performance. In this manuscript, we provide a comprehensive review on face anti-spoofing techniques presented in this joint event and point out directions for future research on the face anti-spoofing field.

This book revises and expands upon the prior edition of Multi-Modal Face Presentation Attack Detection. The authors also discuss the reasons that cause face anti-spoofing to be essential for preventing security breaches in face recognition systems.

This book revises and expands upon the prior edition of Multi-Modal Face Presentation Attack Detection. The authors also discuss the reasons that cause face anti-spoofing to be essential for preventing security breaches in face recognition systems.

Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.