Human Centric Visual Analysis with Deep Learning (eBook)

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.

Liang Lin is a Professor at the School of Data and Computer Science, Sun Yat-sen University (SYSU), China. He received his Bachelor and Ph.D. degree in Computer Science from the Beijing Institute of Technology (BIT), China, in 1999 and 2008, respectively. From 2006 to 2007, he was a joint Ph.D. student at the Department of Statistics, University of California, Los Angeles (UCLA). His research focuses on new models, algorithms and systems for intelligent processing and understanding of visual data. He has been supported by several programs or funds, such as the Ministry of Education (China) 'Program for New Century Excellent Talents' in 2012, and the Guangdong NSFs for Distinguished Young Scholars in 2013. He received the Best Paper Runners-Up Award in ACM NPAR 2010, Google Faculty Award in 2012, and Best Student Paper Award in IEEE ICME 2014.

Dongyu Zhang is a Research Scientist at the School of Data and Computer Science, Sun Yat-sen University (SYSU), China. He received his Master's and Ph.D. degree in Computer Science from the Harbin Institute of Technology (HIT), China, in 2003 and 2008, respectively. His current research interests include deep learning, image modeling and biometrics.

Ping Luo is a Research Assistant Professor at the Chinese University of Hong Kong, where he received his Ph.D. degree in 2014. His research interests focus on machine learning and computer vision, including deep learning optimization and theory, face and pedestrian analysis, image parsing, and large-scale object recognition and detection. Dr. Luo has published more than 60 papers in the top-tier academic journals and conferences, including TPAMI, IJCV, NIPS, ICML, and CVPR. His papers have over 6000 citations in Google Scholar. Because of his contribution in deep learning and computer vision, Dr. Luo was awarded the Microsoft Research Fellowship in 2013. Only ten scholars in the Asia-Pacific area received this award each year. Besides, he was elected the Hong Kong PhD Fellowship in 2011 by the Research Grants Council of Hong Kong.

Wangmeng Zuo is a Professor at the School of Computer Science and Technology, Harbin Institute of Technology (HIT), China. He received his Ph.D. degree in Computer Application Technology from the HIT in 2007. From July 2004 to December 2004, from November 2005 to August 2006, and from July 2007 to February 2008, he was a Research Assistant at the Department of Computing, Hong Kong Polytechnic University. From August 2009 to February 2010, he was a Visiting Professor at Microsoft Research Asia. His current research interests include image restoration, image editing, image classification, object detection, and visual tracking. Dr. Zuo is an Associate Editor of the IET Biometrics, and a Guest Editor of Neurocomputing, Pattern Recognition, and IEEE Transactions on Neural Network and Learning Systems.

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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.