The　illumination　changes　on　face　images　make　face　recognition　a　very　difficult　task.　In　this　paper,　a　human　face　representation　scheme　that　is　insensitive　to　illumination　variation　is　proposed　in　order　to　deal　with　the　problem.　The　variations　in　lighting　over　human　faces　are　modeled　by　means　of　Principal　Component　Analysis　(PCA)　on　a　number　of　blurred　faces　under　different　lighting　conditions.　Then　the　＇difference　image＇,　which　is　the　difference　between　the　original　image　and　the　reconstructed　image,　is　used　for　face　recognition.　We　also　propose　an　uncorrelated　Linear　Discriminant　Analysis　technique　for　face　recognition　based　on　the　eigen-illumination　representation　scheme.　This　method　can　obtain　the　uncorrelated　optimal　discriminant　vectors　(UODVs)　so　that　the　extracted　features　are　uncorrelated.　Experimental　results　show　that　the　proposed　method　is　effective　to　deal　with　varying　illimunation　problem　for　face　recognition.