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The problem of person re-identification is to recognize a target subject across non-overlapping distributed cameras at different times and locations. The applications of person re-identification include security, surveillance, multi-camera tracking, etc. In a real-world scenario, person re-identification is challenging due to the dramatic changes in a subject's appearance in terms of pose, illumination, background, and occlusion. Existing approaches either try to design robust features to identify a subject across different views or learn distance metrics to maximize the similarity between different views of the same person and minimize the similarity between different views of different persons. In this paper, we aim at improving the re-identification performance by reranking the returned results based on soft biometric attributes, such as gender, which can describe probe and gallery subjects at a higher level. During reranking, the soft biometric attributes are detected and attribute-based distance scores are calculated between pairs of images by using a regression model. These distance scores are used for reranking the initially returned matches. Experiments on a benchmark database with different baseline re-identification methods show that reranking improves the recognition accuracy by moving upwards the returned matches from gallery that share the same soft biometric attributes as the probe subject.