The　sparse　representation-based　classifier　(SRC)　has　been　developed　and　verified　as　having　great　potential　for　real-world　face　recognition.　In　this　paper,　we　propose　a　discriminative　projection　and　representation-based　classification　(DPRC)　method　to　enhance　the　discriminant　ability　of　the　SRC.　The　proposed　method　first　obtains　a　discriminative　projection　matrix　not　only　maximizing　the　ratio　of　the　distance　within　interclass　over　the　distance　within　intraclass,　but　also　minimizing　the　linear　approximation　error　within　intraclass.　Then　it　maps　the　original　data　onto　the　discriminative　space,　and　adopts　an　SRC　method　to　obtain　the　final　solution.　An　inexact　augmented　Lagrangian　method　of　multiplier　is　proposed　for　finding　the　optimal　representation　vector　in　our　framework,　and　a　proximal　alternating　minimization　method　is　adopted　to　the　iteration　subproblems　of　the　proposed　method.　The　proposed　method　is　proven　to　have　the　subsequence　convergence　property.　Experimental　results　on　Yale,　ORL,　and　AR　face　image　databases　demonstrate　that,　compared　with　some　existing　feature　extraction　methods　based　on　the　SRC,　the　proposed　DPRC　method　is　more　efficient.