Illumination　variation　is　still　a　challenging　issue　to　address　in　face　recognition.　Retinex　scheme　is　effective　to　face　images　under　small　illumination　variation,　but　its　performance　drop　when　illumination　variation　is　large.　We　further　analyze　the　normalized　images　under　large　illumination　variation　and　we　find　that　the　illumination　variation　has　not　been　removed　thoroughly　in　these　images.　Structural　similarity　is　one　of　image　similarity　metrics　similar　to　human　perception.　From　SSIM,　we　extract　the　structure　related　component　and　name　it　as　Normalized　Structure　Descriptor.　It　is　clear　that　NSD　is　robust　to　illumination　variation.　We　propose　a　scheme　to　combining　Normalized　Structural　Descriptor　with　Single　Scale　Retinex.　In　our　scheme　NSD　is　extracted　from　the　normalized　image　from　SSR.　And　the　face　recognition　is　performed　by　the　similarity　of　NSD.　The　experimental　results　on　the　Yale　Face　Database　B　and　Extended　Yale　Face　Database　B　show　that　our　approach　has　performance　comparable　to　state-of-the-art　approaches.　©　Springer　International　Publishing　2013.