Open AccessStructure Design of Convolutional Neural Network Based on Residual Theory for Face Recognition
Author(s) -
Chenchen Zhang,
Jin Seong Hong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1738/1/012016
Subject(s) - residual , computer science , convolutional neural network , artificial intelligence , deep learning , convolution (computer science) , artificial neural network , pattern recognition (psychology) , feature extraction , neocognitron , face (sociological concept) , facial recognition system , feature (linguistics) , simple (philosophy) , time delay neural network , machine learning , algorithm , social science , linguistics , philosophy , epistemology , sociology
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.