The performance of any biometric recognition system heavily dependents on finding a good
and suitable feature representation space where observations from different classes are well …

Industrial Internet-of-Things (IIoT) has revolutionized almost every aspect of industrial
manufacturing through industrial intelligence by incorporating production equipment, mobile …

Most industrial systems frequently switch their operation modes due to various factors, such
as the changing of raw materials, static parameter setpoints, and market demands. To …

Nowadays, deep learning methods, especially the convolutional neural networks (CNNs),
have shown impressive performance on extracting abstract and high-level features from the …

With the development of synthetic aperture radar (SAR) system, automatic target recognition
(ATR) has attracted wide attention in many decision-making tasks, in which an enhanced …

Abstract Strict '0-1'block-diagonal structure has been widely used for learning structured
representation in face recognition problems. However, it is questionable and unreasonable …

Although there has been a growing body of work for face recognition, it is still a challenging
task for faces under occlusion with limited training samples. In this work, we propose a novel …

In this paper, we investigate the robust dictionary learning (DL) to discover the hybrid salient
low-rank and sparse representation in a factorized compressed space. A Joint Robust …

This work investigates the problem of undersampled face recognition (ie, insufficient training
data) encountered in practical Internet-of-Things (IoT) applications. Insufficient and uncertain …

In this article, a supervised nonlinear dictionary learning (DL) method, called multiscale
supervised kernel DL (MSK-DL), is proposed for target recognition in synthetic aperture …