This　article　presents　a　novel　person　reidentification　model,　named　multihead　self-attention　network　(MHSA-Net),　to　prune　unimportant　information　and　capture　key　local　information　from　person　images.　MHSA-Net　contains　two　main　novel　components:　multihead　self-attention　branch　(MHSAB)　and　attention　competition　mechanism　(ACM).　The　MHSAB　adaptively　captures　key　local　person　information　and　then　produces　effective　diversity　embeddings　of　an　image　for　the　person　matching.　The　ACM　further　helps　filter　out　attention　noise　and　nonkey　information.　Through　extensive　ablation　studies,　we　verified　that　the　MHSAB　and　ACM　both　contribute　to　the　performance　improvement　of　the　MHSA-Net.　Our　MHSA-Net　achieves　competitive　performance　in　the　standard　and　occluded　person　Re-ID　tasks.