© 2017

Facial Kinship Verification

A Machine Learning Approach


Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-x
  2. Haibin Yan, Jiwen Lu
    Pages 1-5
  3. Haibin Yan, Jiwen Lu
    Pages 7-36
  4. Haibin Yan, Jiwen Lu
    Pages 37-62
  5. Haibin Yan, Jiwen Lu
    Pages 63-80
  6. Haibin Yan, Jiwen Lu
    Pages 81-82

About this book


This book provides the first systematic study of facial kinship verification, a new research topic in biometrics. It presents three key aspects of facial kinship verification: 1) feature learning for kinship verification, 2) metric learning for kinship verification, and 3) video-based kinship verification, and reviews state-of-the-art research findings on facial kinship verification.

Many of the feature-learning and metric-learning methods presented in this book can also be easily applied for other face analysis tasks, e.g., face recognition, facial expression recognition, facial age estimation and gender classification. Further, it is a valuable resource for researchers working on other computer vision and pattern recognition topics such as feature-learning-based and metric-learning-based visual analysis.


Kinship Verification Feature Learning Metric Learning Face Analysis Biometrics

Authors and affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Tsinghua UniversityBeijingChina

About the authors

Dr. Haibin Yan received her B.Eng. and M.Eng. degrees from the Xi'an University of Technology, Xi'an, China, in 2004 and 2007, and her Ph.D. degree from the National University of Singapore in 2013, all in mechanical engineering. She is currently an assistant professor at the School of Automation, Beijing University of Posts and Telecommunications, China. From October 2013 to July 2015, she was a research fellow at the Department of Mechanical Engineering, National University of Singapore, Singapore. Her research interests include pattern recognition and robotic vision. She has published more than 20 papers in major journals, such as the IEEE Transactions on Cybernetics, the IEEE Transactions on Information Forensics and Security, and Image and Vision Computing. Her research on facial kinship verification was funded by the National Science Foundation of China in 2016.

Dr. Jiwen Lu received his B.Eng. degree in mechanical engineering and M.Eng. degree in electrical engineering from the Xi'an University of Technology, Xi'an, China, and his Ph.D. degree in electrical engineering from the Nanyang Technological University, Singapore, in 2003, 2006, and 2012, respectively. He is currently an associate professor at the Department of Automation, Tsinghua University, Beijing, China. From March 2011 to November 2015, he was a research scientist at the Advanced Digital Sciences Center, Singapore. His research interests include computer vision, pattern recognition, and machine learning. He has authored/co-authored over 140 scientific papers in these areas, 38 of which were IEEE Transactions papers. He serves/has served as an associate editor of Pattern Recognition Letters, Neurocomputing, and IEEE Access; a managing guest editor of Pattern Recognition and Image and Vision Computing, a guest editor of Computer Vision and Image Understanding, and an elected member of the Information Forensics and Security Technical Committee of the IEEE Signal Processing Society. He is/was a workshop chair/special session chair/area chair for more than 10 international conferences. He was a recipient of the National 1000 Young Talents Plan Program in 2015.

Bibliographic information

  • Book Title Facial Kinship Verification
  • Book Subtitle A Machine Learning Approach
  • Authors Haibin Yan
    Jiwen Lu
  • Series Title SpringerBriefs in Computer Science
  • Series Abbreviated Title SpringerBriefs Computer Sci.
  • DOI
  • Copyright Information The Author(s) 2017
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science Computer Science (R0)
  • Softcover ISBN 978-981-10-4483-0
  • eBook ISBN 978-981-10-4484-7
  • Series ISSN 2191-5768
  • Series E-ISSN 2191-5776
  • Edition Number 1
  • Number of Pages X, 82
  • Number of Illustrations 4 b/w illustrations, 29 illustrations in colour
  • Topics Image Processing and Computer Vision
    Pattern Recognition
  • Buy this book on publisher's site
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