Low-resolution face recognition: a review

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.

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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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Wang, Z., Miao, Z., Jonathan Wu, Q.M. et al. Low-resolution face recognition: a review. Vis Comput 30, 359–386 (2014). https://doi.org/10.1007/s00371-013-0861-x

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Keywords

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