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The Visual Computer

, Volume 30, Issue 4, pp 359–386 | Cite as

Low-resolution face recognition: a review

  • Zhifei WangEmail author
  • Zhenjiang Miao
  • Q. M. Jonathan Wu
  • Yanli Wan
  • Zhen Tang
Original Article

Abstract

Low-resolution face recognition (LR FR) aims to recognize faces from small size or poor quality images with varying pose, illumination, expression, etc. It has received much attention with increasing demands for long distance surveillance applications, and extensive efforts have been made on LR FR research in recent years. However, many issues in LR FR are still unsolved, such as super-resolution (SR) for face recognition, resolution-robust features, unified feature spaces, and face detection at a distance, although many methods have been developed for that. This paper provides a comprehensive survey on these methods and discusses many related issues. First, it gives an overview on LR FR, including concept description, system architecture, and method categorization. Second, many representative methods are broadly reviewed and discussed. They are classified into two different categories, super-resolution for LR FR and resolution-robust feature representation for LR FR. Their strategies and advantages/disadvantages are elaborated. Some relevant issues such as databases and evaluations for LR FR are also presented. By generalizing their performances and limitations, promising trends and crucial issues for future research are finally discussed.

Keywords

Review Face recognition Low-resolution Super-resolution Feature extraction Feature classification 

Notes

Acknowledgements

This work is supported by the National Key Technology R&D Program of China (2012BAH01F03), National Natural Science Foundation of China (60973061), National Basic Research (973) Program of China (2011CB302203), Ph.D. Programs Foundation of Ministry of Education of China (20100009110004), Beijing Natural Science Foundation (4123104), and China Postdoctoral Science Foundation (2013M530020). The authors would like to thank Professor Shengyong Chen from Zhejiang University of Technology and the anonymous reviewers for their comments and suggestions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhifei Wang
    • 1
    • 2
    Email author
  • Zhenjiang Miao
    • 1
    • 2
  • Q. M. Jonathan Wu
    • 3
  • Yanli Wan
    • 4
  • Zhen Tang
    • 1
    • 2
  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  2. 2.Beijing Key Laboratory of Advanced Information Science and Network TechnologyBeijingChina
  3. 3.Department of Electrical and Computer EngineeringUniversity of WindsorWindsorCanada
  4. 4.Institute of System Engineering and ControlBeijing Jiaotong UniversityBeijingChina

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