This book introduces the applications of deep learning in various human
centric visual analysis tasks, including classical ones like face
detection and alignment and some newly rising tasks like fashion
clothing parsing. Starting from an overview of current research in human
centric visual analysis, the book then presents a tutorial of basic
concepts and techniques of deep learning. In addition, the book
systematically investigates the main human centric analysis tasks of
different levels, ranging from detection and segmentation to parsing and
higher-level understanding. At last, it presents the state-of-the-art
solutions based on deep learning for every task, as well as providing
sufficient references and extensive discussions.
Specifically, this book addresses four important research topics,
including 1) localizing persons in images, such as face and pedestrian
detection; 2) parsing persons in details, such as human pose and
clothing parsing, 3) identifying and verifying persons, such as face and
human identification, and 4) high-level human centric tasks, such as
person attributes and human activity understanding.
This book can serve as reading material and reference text for academic
professors / students or industrial engineers working in the field of
vision surveillance, biometrics, and human-computer interaction, where
human centric visual analysis are indispensable in analysing human
identity, pose, attributes, and behaviours for further understanding.
