Illumination　condition　is　one　of　the　most　important　factors　that　affect　the　face　recognition　performance.　Face　image　illumination　quality　assessment　can　predict　the　face　recognition　performance　under　various　illumination　conditions,　which　will　improve　the　accuracy　and　efficiency　of　the　face　recognition　system.　However,　the　quality　scores　calculated　by　the　existing　methods　are　weakly　correlated　with　the　performance　of　face　recognition.　Face　images　with　high　scores　often　present　bad　recognition　performance,　while　face　images　with　low　scores　unexpectedly　present　good　recognition　performance.　To　predict　the　recognition　performance　more　accurately,　a　kernel　partial　least　squares　regression　(KPLSR)　based　face　image　illumination　quality　assessment　method　for　surveillance　video　is　proposed　in　this　paper.　The　mapping　relationship　between　illumination　conditions　and　face　recognition　performance　is　modeled　using　KPLSR.　Taken　the　fact　that　different　regions　of　face　image　have　different　importance　on　face　recognition,　the　features　of　luminance　and　contrast　of　sub-blocks　are　extracted　to　reflect　the　illumination　conditions.　Matching　score　of　the　face　recognition　system　is　calculated　to　measure　the　recognition　performance.　Experimental　results　show　that,　compared　with　the　existing　methods,　the　quality　scores　calculated　by　the　proposed　method　are　strong　correlated　with　the　recognition　performance.　The　proposed　method　can　also　meet　the　requirements　of　real　time　processing.