Low-resolution face recognition: a review

  • Authors:

  • Zhifei Wang;Zhenjiang Miao;Q. M. Jonathan Wu;Yanli Wan;Zhen Tang

  • Affiliations:

  • Institute of Information Science, Beijing Jiaotong University, Beijing, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China;Institute of Information Science, Beijing Jiaotong University, Beijing, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China;Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada;Institute of System Engineering and Control, Beijing Jiaotong University, Beijing, China;Institute of Information Science, Beijing Jiaotong University, Beijing, China and Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China

  • Venue:
  • The Visual Computer: International Journal of Computer Graphics
  • Year:
  • 2014

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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.