Different　environment　illumination　has　a　great　impact　on　face　detection.　In　this　paper,　we　present　a　solution　by　the　face　relighting　based　on　the　harmonic　images.　The　basic　idea　is　that　there　exist　nine　harmonic　images　which　can　be　derived　from　a　3D　model　of　a　face,　and　by　which　we　can　estimate　the　illumination　coefficient　of　any　face　samples.　To　detect　faces　under　the　certain　lighting　conditions,　we　relight　those　original　face　samples　to　get　more　new　face　samples　under　the　various　possible　lighting　conditions　by　an　illumination　ratio　image　and　then　add　them　to　the　training　set.　By　train　a　classifier　based　on　Support　Vector　Machine　(SVM),　the　experimental　results　turn　out　that　the　relighting　subspace　is　effective　during　the　detection　under　the　diverse　lighting　conditions.　We　also　use　the　relighting　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.