In　recent　years,　Sparse　Representation　based　classification　(SRC)　has　made　great　progress　in　Face　Recognition.　However,　SRC　is　only　efficient　and　effective　when　the　noise　is　sparse.　The　recognition　rate　of　SRC　decreases　when　the　noise　is　non-Gaussian,　for　example,　the　light　on　the　face　is　quite　various　or　the　face　is　covered　in　part　by　a　mask.　In　this　paper,　we　propose　a　robust　l(2,1)-norm　Sparse　Representation　frameworkthat　constrains　the　noise　penalty　by　the　l(2,1)-norm.　Thisframework　takes　both　advantages　of　the　discriminative　nature　of　the　l(*)-norm　and　the　systemic　representation　of　the　l(2,1)　-norm.　Inaddition,　we　also　use　the　l(2,1)　-norm　to　constrain　the　coefficientmatrix.　As　the　l(*)-norm　concerns　the　global　structure,　our　methodis　robust　to　the　noise,　especially　for　the　case　when　the　contiguousocclusion　exists　in　the　real　world.　The　extensive　experiments　demonstrate　that　when　dealing　with　large　region　contiguous　occlusion,　the　proposed　method　achieves　significantly　better　results　than　SRC　and　some　other　sparse　representation　based　face　recognition　methods.