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Person re-identification is to match person images observed from different camera views of non-overlapping multi-camera surveillance systems. The person images are easily affected by illumination changes, different viewpoints and varying poses, it is likely to form a lot of differences in appearance. For the above problem, this study proposed a dense horizontal stripes and kernel space mapping for person re-identification. First, the each horizontal stripe of person images is extracted from color features and a texture feature by using the top-down sliding horizontal stripe. Then, multi-features of person images fusion and kernel space mapping. Finally, the algorithm gets a similarity function which is robust to the change of background, viewpoint and posture by learning in kernel space. The proposed method is demonstrated on two public benchmark datasets including VIPeR and iLIDS, and experimental results show that the proposed method achieves excellent re-identification rates compared with other similar algorithms. Moreover, the proposed method achieves a 48.2% at rank1 (represents the correct matched pair) on VIPeR benchmark and a 60.8% at rank1 on iLIDS benchmark respectively.