Structure Design of Convolutional Neural Network Based on Residual Theory for Face Recognition

Abstract

Compared with the traditional face recognition methods, the deep convolution neural network model does not need to manually design complex and time-consuming feature extraction algorithms, but only needs to design an effective neural network model, and then carry out end-to-end, simple and efficient training on a large number of training samples, so as to obtain better classification accuracy. In this paper, based on the original VGG network model, combined with Residual theory, a deep-level residual convolutional neural network structure is designed and implemented, which not only reduces the computing power requirements of hardware computers, but also achieves better recognition results.