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

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 quantit...

Full description

Saved in:
Bibliographic Details
Published in:IEEE internet of things journal Vol. 8; no. 24; pp. 17320 - 17332
Main Authors: Yang, Shicheng, Wen, Ying, He, Lianghua, Zhou, MengChu, Abusorrah, Abdullah
Format: Journal Article
Language:English
Published: Piscataway IEEE 15.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Classification
Datasets
Dictionaries
Face recognition
Feature extraction
Image reconstruction
Internet of Things
Lagrange multiplier
Mathematical models
Neural networks
Polynomials
Representations
sparse individual low-rank component representation
sparse representation
Training
Training data
ISSN:2327-4662, 2327-4662
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formula>-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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2021.3080084