Because a large number of useless features exist in the image of person re-identification due to occlusion and background interference, a person re-identification method based on low-pass filtering model was proposed. First, the person images were divided into blocks. Then the similar number of small blocks in each image were calculated. Among them, the blocks with higher similarity number were marked as high-frequency noise features and the blocks with smaller similarity number were the beneficial features. Finally, different from the low-pass filter which filtered the mutation features and maintained the smooth features in the common image processing, the low-pass filter in the communication system was used to achieve the goal of suppressing high-frequency noise features and gain beneficial features in the proposed method. Experimental results show that the identification rate of the proposed method on ETHZ dataset is nearly 20% higher than that of the classic Symmetry-Driven Accumulation of Local Features (SDALF) method, and at the same time, this method achieves similar results on VIPeR (Viewpoint Invariant Pedestrian Recognition) and I-LIDS (Imagery Library for Intelligent Detection Systems) datasets.