Current deep learning-based texture recognition methods extract spatial orderless features
from pre-trained deep learning models that are trained on large-scale image datasets …

Automatic facial expression recognition (FER) has been extensively studied owing to its
wide range of applications, such as in e-learning platforms used to automatically collect the …

Face recognition applications focus on local features to prevent detailed information from
being omitted while the feature extraction processes. This paper is based on presenting a …

Binary pattern family is considered as a powerful tool for visual texture classification. Most
popular methods improve the classification performance by multi-feature fusion. However …

Classifying and modeling texture images, especially those with significant rotation,
illumination, scale, and view-point variations, is a hot topic in the computer vision field …

Many variations of local binary pattern (LBP) were proposed to enhance its performance,
including uniform local binary pattern (ULBP), center-symmetric local binary patterns (CS …

In this paper, a new approach for texture classification called wavelet domain majority
coupled binary pattern is proposed. Here, the single-level wavelet transform is applied …

In this letter, we propose a simple yet effective texture descriptor, symmetric mean and
directional contour pattern (SMDCP), for texture classification. In particular, first the robust …

To realize effective feature representation, this paper proposes a new complex networks
(CNs)-based feature fusion scheme to recognize texture images. Specifically, we propose …

To overcome the limitations of original local binary patterns (LBPs), this article proposes a
new texture descriptor aided by complex networks (CNs) and LBPs, named CN-LBP …