Active　Shape　Model　(ASM)　has　been　widely　recognized　as　one　of　the　best　methods　for　image　understanding.　In　this　paper,　we　propose　to　enhance　ASMs　by　introducing　global　texture　constraints　expressed　by　its　reconstruction　residual　in　the　texture　subspace.　In　the　proposed　method,　each　landmark　is　firstly　matched　by　its　local　profile　in　its　current　neighborhood,　and　the　overall　configure　of　all　the　landmarks　is　re-shaped　by　the　statistical　shape　constraint　as　in　the　ASMs.　Then,　the　global　texture　is　warped　out　from　the　original　image　according　to　the　current　shape　model,　and　its　reconstruction　residual　from　the　pre-trained　texture　subspace　is　further　exploited　to　evaluate　the　fitting　degree　of　the　current　shape　model　to　the　novel　image.　Also,　the　texture　is　exploited　to　predict　and　update　the　shape　model　parameters　before　we　turn　to　the　next　iterative　local　matching　for　each　landmark.　Our　experiments　on　the　facial　feature　analysis　have　shown　the　effectiveness　of　the　proposed　method.　©　Springer-Verlag　Berlin　Heidelberg　2003.