The　low-rank　(LR)　property　is　widely　applied　to　capture　the　global　as　well　as　intrinsic　structure　of　the　given　data　in　different　visual　information　processing　tasks.　Actually,　there　are　three　key　information　to　determine　the　performance　and　generalization　low-rank　property　based　methods:　(1)　visual　intrinsic　structural　information,　(2)　visual　representation　structural　information,　(3)　visual　robust　information.　To　achieve　these　jointly　in　a　unified　framework,　in　this　paper,　we　propose　Dual　Low-rank　Structure　Embedding　(DLSE)　that　embeds　structural　and　robust　information.　We　additionally　proposed　the　Joint　Matrix-based　Linear　Representation　(JMLR)　and　theoretically　proved　it　can　realize　DLSE.　The　proposed　method　was　validated　on　6　datasets　(from　1,440　samples　to　70,000　samples　in　size)　and　showed　a　promising　performance　in　visual　recognition　(14.32%　improvement　compared　with　the　deep　features).　In　addition,　we　performed　multiple　analysis　on　robustness,　representation,　and　its　parameters　to　show　the　effectiveness　of　DLSE　from　different　aspects.　©　2024　Elsevier　B.V.