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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.
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 investigates the problem of facial image analysis. Human
faces contain a lot of information that is useful for many
applications. For instance, the face and iris are important
biometric features for security applications. Facial activity
analysis such as face expression recognition is helpful for
perceptual user interfaces. Developing new methods to improve
recognition performance, in terms of face, iris, and facial
expression, is a major concern in the presentation.
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.
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