Learning sparse discriminant low-rank features for low-resolution face recognition

M. Saad Shakeel, Kin Man Lam, Shun Cheung Lai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


In this paper, we propose a novel approach for low-resolution face recognition, under uncontrolled settings. Our approach first decomposes a multiple of extracted local features into a set of representative basis (low-rank matrix) and sparse error matrix, and then learns a projection matrix based on our proposed sparse-coding-based algorithm, which preserves the sparse structure of the learned low-rank features, in a low-dimensional feature subspace. Then, a coefficient vector, based on linear regression, is computed to determine the similarity between the projected gallery and query image's features. Furthermore, a new morphological pre-processing approach is proposed to improve the visual quality of images. Our experiments were conducted on five available face-recognition datasets, which contain images with variations in pose, facial expressions and illumination conditions. Experiment results show that our method outperforms other state-of–the-art low-resolution face recognition methods in terms of recognition accuracy.

Original languageEnglish
Article number102590
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - Aug 2019


  • Face recognition
  • Feature fusion
  • Linear regression
  • Local features
  • Low rank approximation
  • Sparse coding

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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