The　appearance　of　a　face　will　vary　drastically　when　the　illumination　changes.　Variations　in　lighting　conditions　make　face　recognition　an　even　more　challenging　and　difficult　task.　In　this　paper,　we　propose　a　novel　approach　to　handle　the　illumination　problem.　Our　method　can　restore　a　face　image　captured　under　arbitrary　lighting　conditions　to　one　with　frontal　illumination　by　using　a　ratio-image　between　the　face　image　and　a　reference　face　image,　both　of　which　are　blurred　by　a　Gaussian　filter.　An　iterative　algorithm　is　then　used　to　update　the　reference　image,　which　is　reconstructed　from　the　restored　image　by　means　of　principal　component　analysis　(PCA),　in　order　to　obtain　a　visually　better　restored　image.　Image　processing　techniques　are　also　used　to　improve　the　quality　of　the　restored　image.　To　evaluate　the　performance　of　our　algorithm,　restored　images　with　frontal　illumination　are　used　for　face　recognition　by　means　of　PCA.　Experimental　results　demonstrate　that　face　recognition　using　our　method　can　achieve　a　higher　recognition　rate　based　on　the　Yale　B　database　and　the　Yale　database.　Our　algorithm　has　several　advantages　over　other　previous　algorithms:　(1)　it　does　not　need　to　estimate　the　face　surface　normals　and　the　light　source　directions,　(2)　it　does　not　need　many　images　captured　under　different　lighting　conditions　for　each　person,　nor　a　set　of　bootstrap　images　that　includes　many　images　with　different　illuminations,　and　(3)　it　does　not　need　to　detect　accurate　positions　of　some　facial　feature　points　or　to　warp　the　image　for　alignment,　etc.　(c)　2005　Pattern　Recognition　Society.　Published　by　Elsevier　Ltd.　All　rights　reserved.