Person　re-identification　aims　at　matching　individuals　across　multiple　non-overlapping　adjacent　cameras.　By　condensing　multiple　gallery　images　of　a　person　as　a　whole,　we　propose　a　novel　method　named　Set-Label　Model　(SLM)　to　improve　the　performance　of　person　re-identification　under　the　multi-shot　setting.　Moreover,　we　utilize　mutual-information　to　measure　the　relevance　between　query　image　and　gallery　sets.　To　decrease　the　computational　complexity,　we　apply　a　Naive-Bayes　Nearest-Neighbor　algorithm　to　approximate　the　mutual-information　value.　To　overcome　the　limitations　of　traditional　linear　metric　learning,　we　further　develop　a　deep　non-linear　metric　learning　(DeepML)　approach　based　on　Neighborhood　Component　Analysis　and　Deep　Belief　Network　To　evaluate　the　effectiveness　of　our　proposed　approaches,　SLM　and　DeepML,　we　have　carried　out　extensive　experiments　on　two　challenging　datasets　LIDS　and　ETHZ.　The　experimental　results　demonstrate　that　the　proposed　methods　can　obtain　better　performances　compared　with　the　state-of-the-art　methods.　(C)　2014　Elsevier　B.V.　All　rights　reserved.