Face-detection　methods　based　oil　cascade　architecture　have　demonstrated　a　fast　and　robust　performance.　In　most　of　these　methods,　each　node　of　the　cascade　employs　the　simple　Haar-like　features　from　the　central　eye-nose-mouth　region　using　the　boosting　method.　However,　it　can　be　empirically　observed　that,　in　the　deeper　nodes　of　the　boosting　process,　the　non-face　examples　collected　by　bootstrapping　are　in　fact　very　similar　to　the　face　examples,　and　the　error　rate　of　those　feature-based　weak　classifiers　is　very　close　to　50%.　Consequently,　the　performance　of　the　face　detector　is　hardly　further　improved.　In　this　paper,　we　propose　a　novel　and　simple　Solution　to　this　problem　by　imitating　the　characteristics　of　the　human　visual　system.　The　main　idea　of　our　solution　is　to　boost　the　cascade　based　oil　a　hierarchical　strategy,　which　employs　the　information　from　the　central　and　surrounding　parts　of　the　face　regions　step　by　step.　We　argue　that　the　context　information　about　a　face　can　be　advantageously　used　in　the　deeper　nodes　of　the　boosting　process　when　the　features　derived　from　the　central　region　of　the　face　do　not　provide　any　further　benefit.　Furthermore,　we　also　propose　a　simplified　Gabor　feature　to　extend　the　feature　Set　for　the　training　of　deeper　nodes.　Experiments　Show　that　Our　proposed　method　can　improve　not　only　the　detection　performance,　but　also　the　detection　speed,　by　about　10%　when　compared　to　the　original　AdaBoost　face-detection　method　for　our　implementation.　(C)　2009　Elsevier　B.V.　All　rights　reserved.