Low-resolution face recognition and the importance of proper alignment

Yuxy Peng, Luuk Spreeuwers, Raymond N.J. Veldhuis*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

Face recognition methods for low resolution are often developed and tested on down-sampled images instead of on real low-resolution images. Although there is a growing awareness that down-sampled and real low-resolution images are different, few efforts have been made to analyse the differences in recognition performance. Here, the authors explore the differences and demonstrate that alignment is a major cause, especially in the absence of pose and illumination variations. The authors found that the recognition performances on down-sampled images are flattered mostly due to the fact that the images are perfectly aligned before down-sampling using high-resolution landmarks, while the real low-resolution images have much poorer alignment. To obtain better alignment for real low-resolution images, the authors apply matching score-based registration which does not rely on accurate landmarks. The authors propose to divide low resolution into three ranges to harmonise the terminology: upper low resolution (ULR), moderately low resolution (MLR), and very low resolution (VLR). Most face recognition methods perform well on ULR. MLR is a challenge for commercial systems, but a low-resolution deep-learning method can handle it very well. The performance of most methods degrades significantly for VLR, except for simple holistic methods which perform the best.

Original languageEnglish
Article number18726042
Pages (from-to)267-276
Number of pages10
JournalIET biometrics
Volume8
Issue number4
Early online date20 Feb 2019
DOIs
Publication statusPublished - 17 Jun 2019

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Keywords

  • Face recognition
  • Image matching
  • Image registration
  • Image resolution
  • Image sampling
  • Learning (artificial intelligence)
  • Pose estimation

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