Different　environment　illuminations　have　a　great　impact　on　face　detection.　In　this　paper,　we　present　a　solution　based　on　face　relighting　technology.　The　basic　idea　is　that　there　exists　nine　harmonic　images　that　can　be　derived　from　a　3D　model　of　a　face,　and　by　which　we　can　estimate　the　illumination　coefficient　of　any　face　samples.　Using　an　illumination　radio　image,　we　can　produce　images　under　new　lighting　conditions.　To　detect　faces　under　certain　lighting　condition,　we　relight　original　face　samples　to　get　more　new　faces　under　kinds　of　possible　lighting　condition　and　add　them　to　the　training　set.　Our　experimental　results　on　Support　Vector　Machine　(SVM)　turn　out　that　the　relighting　subspace　is　effective　on　detection　under　variations　of　the　lighting　conditions.　Moreover,　if　we　relight　original　face　samples　to　new　samples　under　different　illuminations,　the　collected　example　sets　will　be　multiplied.　We　use　the　expanded　database　to　train　an　AdaBoost-based　face　detector　and　test　it　on　the　MIT+CMU　frontal　face　test　set.　The　experimental　results　show　that　the　data　collection　can　be　efficiently　speeded　up　by　the　proposed　methods.　The　later　experiment　also　verifies　the　generalization　capability　of　the　proposed　method.