Aiming at the problem that in natural scenes

face recognition becomes more difficult due to the interference of posture changes

side faces and other factors

an improved residual neural network is proposed

which optimizes the original network model structure and introduces an attention mechanism to enhance channel important feature acquisition. At the same time

depth residual equivariant mapping module is added

and the function of mapping the side face features into the positive face features in the depth feature space layer is realized without adding too many parameters

further

realize human face recognition rate. The experimental results show that the proposed novel neural network is better than the existing residual neural network on commonly used face datasets such as LFW

CFP

IJB-A

etc.

and improves the face recognition effect under unconstrained conditions.