Person re-identification (re-ID) is an active task with several challenges such as variations of poses, view points, lighting and occlusion. When considering person re-ID as an image retrieval process, measuring the appearance similarity of a pairwise person images is the essential phase. Re-ranking process can improve its accuracy especially when it is based on an other similarity metric. In this paper, we propose a pipeline composed of two methods: A Siamese Convolutional Neural Network (S-CNN) and a k-reciprocal nearest neighbors (k-RNN) re-ranking algorithm. While most existing re-ranking methods ignore the importance of original distance in re-ranking, we jointly combine the S-CNN similarity measure and Jaccard distance to revise the initial ranked list. An experimental study is conducted on two benchmark person re-ID datasets (Market-1501 and Duke-MTMC-reID). The obtained results confirm the effectiveness of our method. A mAP improvement of 11.6% and 15.68% is obtained respectively for the two testing datasets.