Deep neural network (DNN) has been used as a learning model for modeling the
hierarchical architecture of human brain. However, DNN suffers from problems of learning …

Sparse representation has shown an attractive performance in a number of applications.
However, the available sparse representation methods still suffer from some problems, and …

Aiming at the difficult problem of plant leaf recognition on the large-scale database, a two-
stage local similarity based classification learning (LSCL) method is proposed by combining …

In recent years, convolutional neural networks have proven to be a highly efficient approach
for face recognition. In this paper, we develop such a framework to learn a robust face …

Collaborative representation is an effective way to design classifiers for many practical
applications. In this paper, we propose a novel classifier, called the prior knowledge-based …

This paper presents a highly distinctive local surface feature called the TriSI feature for
recognizing 3D objects in the presence of clutter and occlusion. For a feature point, we first …

In most previous cost-sensitive feature extraction methods, the image matrix needs to be
converted into vectors. The conversion always leads to a high computation complexity and …

In recent years, deep networks has achieved outstanding performance in computer vision,
especially in the field of face recognition. In terms of the performance for a face recognition …

One of the significant task in pattern recognition and computer vision along with artificial
intelligence and machine learning is the Face Recognition. Most of the prevailing …

The face almost always has an axis-symmetrical structure. However, as the face usually
does not have an absolutely frontal pose when it is imaged, the majority of face images are …