A　multi-view　face　detection　method　for　Video　surveillance　is　proposed　in　this　paper.　It　provides　the　ability　to　extract　high-level　features　in　terms　of　human　activities　rather　than　low-level　features　like　color,　texture　and　shape.　The　method　is　capable　of　locating　human　faces　over　a　broad　range　of　views　in　color　image　sequences　or　videos　with　complex　scenes.　Firstly,　an　improved　frame　difference　is　used　to　acquire　promising　regions　of　the　image.　Then　it　uses　the　presence　of　skin-tone　pixels　to　locate　faces.　Finally,　an　improved　method　based　on　wavelet　invariant　moment　and　BPNN　is　used　to　verify　the　candidate　face　regions.　The　experimental　results　show　that　the　proposed　algorithm　has　high　speed　and　low　error-detection　rate,　so　it　can　be　used　in　the　real-time　video　surveillance　system.　The　main　distinguishing　contribution　of　this　work　is　being　able　to　detect　faces　irrespective　of　their　poses　by　using　the　wavelet　invariant　moments　as　input　of　the　BPNN,　whereas　contemporary　systems　deal　with　frontal-view　faces　only.　The　other　novel　aspect　of　the　work　lies　in　its　accuracy　of　acquiring　the　candidate　area　to　segment　objects　from　background　with　the　help　of　motion　information　and　skin　information.