Sparse Individual Low-Rank Component Representation for Face Recognition in the IoT-Based System

Shicheng Yang, Ying Wen, Lianghua He, Mengchu Zhou, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


The performance of face recognition has been greatly improved by deep neural network algorithms when a dataset is large. However, when face data are insufficient as in practical Internet of Things (IoT) applications and captured by IoT devices under the same intrasubject variation, both data quantity and quality bring big challenges to construct a model or representation, and most of the time it becomes infeasible to build a deep neural network model. This work proposes a sparse individual low-rank component-based representation (SILR) such that the representation of testing images can be based on individual subjects' low-rank component. Theoretically, we put the l2-norm constraint on intrasubject coefficients to represent testing images, thus making intrasubject coefficients dense. Hence, we alleviate the impact of an undersampled training dataset and its same intersubject variation on classification performance. We solve a convex minimization problem in polynomial time via an augmented lagrange multiplier scheme to get the solution of SILR. The scheme can reduce the influences from the same intersubject variation and contribute to an accurate recognition of the undersampled training dataset. We adopt sparse individual low-rank component representation and minimum reconstruction residual to recognize testing images. Extensive results on various databases show that SILR outperforms the other state-of-the-art methods for face recognition.

Original languageEnglish (US)
Pages (from-to)17320-17332
Number of pages13
JournalIEEE Internet of Things Journal
Issue number24
StatePublished - Dec 15 2021

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Classification
  • Face recognition
  • Sparse individual low-rank component representation
  • Sparse representation


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