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.
Published Version
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