Most　existing　person　re-identification　methods　are　mainly　based　on　human　part　partition　with　horizontal　stripes　or　human　body　semantic　segmentation.　In　this　paper,　we　propose　a　method　called　MDRS　(Multiscale　Discriminative　network　with　Region　Segmentation)　to　integrate　multi-scale　discriminative　feature　learning,　horizontal　stripe　partition　and　semantic　segmentation　in　a　single　framework,　in　which　multiscale　horizontal　stripe　partition　and　usage　of　both　global　and　local　features　make　the　framework　be　robust　to　human　pose　variation,　occlusion　and　background　clutter,　and　semantic　segmentation　boosts　the　performance　of　person　identification　via　shared　multi-scale　feature　extraction.　MDRS　is　trained　end-to-end　with　a　multi-task　learning　strategy　that　considers　three　tasks　simultaneously:　person　identification,　triplet　prediction　and　pixel-wise　semantic　segmentation.　Comprehensive　experiments　confirm　that　our　approach　exceeds　many　methods　and　robustly　achieves　excellent　performances　on　mainstream　evaluation　datasets　including　Market-1501,　DukeMTMC-reid　and　CUHK03.　(C)　2020　Elsevier　B.V.　All　rights　reserved.