The application scenarios of face recognition are very wide, and it has become a research hotspot in the field of computer vision. The deep learning technology represented by the convolutional neural network has shown great advantages in the field of face recognition. Its network structure is developing in a deeper and wider direction, and has achieved better and better recognition results. Due to its complex structure, the training is difficult to converge and occupies a large amount of memory and takes a long time to load, which is not conducive to the application in practical engineering. In order to solve this problem, this paper studies the application of deep learning in the field of face recognition, and proposes an improved residual neural network. On the basis of the original model, this model sacrifices less accuracy, and improves the speed of face recognition to a greater extent. This makes the model of great significance in the field of engineering practice.
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