Deep　learning　methods　show　strong　ability　in　extracting　high-level　features　for　images　in　the　field　of　person　re-identification.　The　produced　features　help　inherently　distinguish　pedestrian　identities　in　images.　However,　on　deep　learning　models　over-fitting　and　discriminative　ability　of　the　learnt　features　are　still　challenges　for　person　re-identification.　To　alleviate　model　over-fitting　and　further　enhance　the　discriminative　ability　of　the　learnt　features,　we　propose　siamese　pedestrian　alignment　networks　(SPAN)　for　person　re-identification.　SPAN　employs　two　streams　of　PAN　(pedestrian　alignment　networks)　to　increase　the　size　of　network　inputs　over　limited　training　samples　and　effectively　alleviate　network　over-fitting　in　learning.　In　addition,　a　verification　loss　is　constructed　between　the　two　PANs　to　adjust　the　relative　distance　of　two　input　pedestrians　of　the　same　or　different　identities　in　the　learned　feature　space.　Experimental　verification　is　conducted　on　six　large　person　re-identification　data　sets　and　the　experimental　results　demonstrate　the　effectiveness　of　the　proposed　SPAN　for　person　re-identification.