… network fusion can significantly improve the face recognition performance because different
networks capture the information from different regions and scales to form a powerful face …

… This method had reached an accuracy of 91% on ORL face database. To solve the face
recognition problem, this paper proposed a SIAMESE convolutional neural network which was …

… deep models for face recognition in images and videos, we have also compared recent
algorithms for face recognition. … Introduction of Deep Neural Network for Face recognition and …

… overview of face recognition, an overview of the field of representation learning and deep
learning, a look at how those fields are influencing the state-of-the-art of face recognition, and …

… The AR face database used to evaluate the approaches’ results of the study showed that
deep learning approaches provide a good result in terms of recognizing faces and pre-…

… II will describe non-deep learning methods by analyzing the … of Deep Learning approach
which used in face recognition by … comparison of Traditional Methods and Deep Learning with …

… tool for face recognition named Local Binary Pattern Network (LBPNet) … deep learning
architecture. LBPNet retains a similar topology of CNN while avoiding the costly model learning on …

… on more recently proposed deep learning methods, which were developed in response to
the limitations of HoG and Haar wavelet features in capturing salient facial information under …

… to improve recognition accuracy. Our objective is to enhance 2D face recognition accuracies
based on convolution neural network which consists of 15 layers to learn discriminative …

… latest deep learning technology to design face recognition system, in order to further … face
recognition methods (facial geometric feature based method, template matching based method…