In　recent　years,　sparse　representation-based　classification　(SRC)　has　made　great　progress　in　face　recognition　(FR).　However,　SRC　emphasizes　noise　sparsity　too　much　and　it　is　not　suitable　for　the　real　world.　In　this　paper,　we　propose　a　robust　-norm　Sparse　Representation　framework　that　constrains　the　noise　penalty　by　the　-norm.　The　-norm　takes　advantage　of　both　the　discriminative　nature　of　the　-norm　and　the　systemic　representation　of　the　-norm.　In　addition,　we　use　the　nuclear　norm　to　constrain　the　coefficient　matrix.　Motivated　by　the　Fisher　criterion,　we　propose　the　Fisher　discriminant-based　-norm　sparse　representation　method　for　FR　which　utilizes　a　supervised　approach.　Thus,　we　consider　the　within-class　scatter　and　between-class　scatter　when　all　of　the　label　information　is　available.　The　paper　shows　that　the　model　can　provide　stronger　discriminant　power　than　the　classical　sparse　representation　models　and　can　be　solved　by　the　alternating　direction　method　of　multiplier.　Additionally,　it　is　robust　to　the　contiguous　occlusion　noise.　Extensive　experiments　demonstrate　that　our　method　achieves　significantly　better　results　than　SRC　and　some　other　sparse　representation　methods　for　FR　when　addressing　large　regions　with　contiguous　occlusion.