Human ear features were widely used in the field of identity recognition in recent years owing to their good uniqueness and stability.However,the problem that human ear acquisition is easily occluded by hair,earrings,and other objects limits the practical application of human ear biometrics.Here,an ear recognition method based on ERNet is proposed by introducing an improved SE module into the IResNet network.Though integrating the statistical properties of max pooling and mean pooling,this method enhances the representation of identity-related features,suppresses the influence of non-correlated features,and solves the problem that human ears are difficult to recognize due to occlusion in uncontrolled environments.Experimental results indicate that the recognition performance of the improved method is significantly improved compared to that of original network. Meanwhile,the proposed model has good robustness under the same occlusion conditions.