Face　recognition　under　complex　illumination　conditions　is　still　an　open　question.　To　cope　with　the　problem,　this　paper　proposes　an　effective　illumination　normalization　method.　The　proposed　method　employs　morphology　and　quotient　image　techniques　by　analyzing　the　face　illumination,　and　it　is　upgraded　with　dynamical　lighting　estimation　technique　to　strengthen　illumination　compensation　and　feature　enhancement.　Compared　with　traditional　approaches,　this　method　doesn＇t　need　any　training　data　and　any　assumption　on　the　light　conditions,　moreover,　the　enrollment　requires　only　one　image　for　each　subject.　The　proposed　methods　are　evaluated　on　Yale　Face　database　B　and　receive　a　very　competitive　recognition　rate　with　low　computational　cost.