Shi Jinn Horng
National Taiwan University of Science and Technology, Taiwan
Title: Appling both hybrid restricted Boltzmann machine and deep convolution neural networks to low-resolution face image recognition
Biography
Biography: Shi Jinn Horng
Abstract
Due to the difficulty of finding the specific features of faces, in computer vision, low-resolution face image recognition is one of the challenging problems and the accuracy of recognition is still quite low. We were trying to solve this problem using deep learning techniques. Two major parts are used for the proposed method; first the restricted Boltzmann machine is used to preprocess the face images, then the deep convolution neural network is used to do classification. The data set was combined from the Georgia Institute of Technology, Aleix Martinez, and Robert Benavente. Based on this combined data, we conducted the training and testing processes. The proposed method is the first method that combines restricted Boltzmann machine and deep convolution neural networks to do low-resolution face image recognition. From the experimental results, compared to existing methods, the proposed method greatly improves the accuracy of recognition. The proposed method is shown in Figure 1. The experimental results are shown in Table 1.
Image
