Sparse Individual Low-rank Component Representation for Face Recognition in IoT-based System

Shicheng Yang, Ying Wen, Lianghua He, Meng Chu Zhou, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


As a classic topic, 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 intra-subject 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 paper 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 intra-subject coefficients to represent testing images, thus making intra-subject coefficients dense. Hence, we alleviate the impact of an undersampled training dataset and its same inter-subject variation on classification performance. We solve a convex minimization problem in polynomial time via an Augmented Lagrange Multiplier scheme to get the solution of the proposed SILR. The scheme can reduce the influences from the same inter-subject 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 benchmark face databases and many non-standard datasets show that the proposed SILR is better than the other state-of-the-art methods for face recognition.

Original languageEnglish (US)
JournalIEEE Internet of Things Journal
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

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


  • Dictionaries
  • Face recognition
  • Face recognition
  • Feature extraction
  • Internet of Things
  • IoT-based systems.
  • Testing
  • Training
  • Training data
  • classification
  • sparse individual low-rank component representation
  • sparse representation


Dive into the research topics of 'Sparse Individual Low-rank Component Representation for Face Recognition in IoT-based System'. Together they form a unique fingerprint.

Cite this