Person re-identification is a very challenging problem and has practical application value. It plays an important role in video surveillance systems because it can reduce human efforts in searching for a target from a large number of videos. However

the pedestrian's image is easily affected by illumination changes

different viewpoints

varying poses

complicated background and the problem of occlusion and scale. It is likely to form a lot of differences in appearance and that causes interference in person re-identification. To solve this problem

many studies concentrate on designing a feature representation or metric learning method. For the above problem

this study proposes a robustness algorithm based on multi-features fusion and independent metric learning for person re-identification. First

the original images are processed by image enhancement algorithm to reduce the impact of illumination changes. This enhancement algorithm is committed to making the image closer to the human visual characteristics. Then

using the method of non-uniform segmentation processes images. At the same time

processed images are extracted from four color features including HSV

RGS

LAB and YCbCr feature and a texture feature of SILTP (scale invariant local ternary pattern).What's more

through multi-features fusion and independent metric learning

the algorithm gets a similarity measure function of the related person. Finally

the algorithm weights the original similarity and gets the ultimate similarity achieving person re-identification. The proposed method is demonstrated on three public benchmark datasets including VIPeR

iLIDS and CHUK01. Each dataset has its own different characteristics. And experimental results show that the proposed method achieves a higher accuracy rate with excellent features and particular method of fusion and learning compared with other similar algorithms. The proposed method achieves a 42.7% rank-1 (represents the correct matched pair) on VIPeR benchmark and respectively 43.6% and 43.7% on iLIDS and CHUK01 benchmark. It is worth mentioning that the rank-5 (represents the expectation of the matches at rank 5) are more than 70% on the three datasets. It greatly improves the recognition rate and has practical application value. The experimental results show that the proposed method can more effectively express pedestrian's image information. Furthermore

the proposed method has strong robustness to illumination changes

different viewpoints

varying poses

complicated background and the problem of occlusion and scale and effectively improves the recognition rate.