This　paper　is　focused　on　cellular　phone　embedded　eye　location　system.　The　proposed　eye　detection　system　is　based　on　a　hierarchy　cascade　FloatBoost　classifier　combined　with　an　MLP　neural　net　post　classifier.　The　system　firstly　locates　the　face　and　eye　candidates＇　areas　in　the　whole　image　by　a　hierarchical　FloatBoost　classifier.　Then　geometrical　and　relative　position　information　of　eye-pair　and　the　face　are　extracted.　These　features　are　input　to　a　MLP　neural　net　post　classier　to　arrive　at　an　eye/non-eye　decision.　Experimental　results　show　that　our　cellular　phone　embedded　eye　detection　system　can　accurately　locate　double　eyes　with　less　computational　and　memory　cost.　It　runs　at　400ms　per　image　of　size　256×256　pixels　with　high　detection　rates　on　a　SANYO　cellular　phone　with　ARM926EJ-S　processor　that　lacks　floating-point　hardware.　©　Springer-Verlag　Berlin　Heidelberg　2006.