Craniofacial　reconstruction　from　skull　has　deeply　been　investigated　by　computer　scientists　in　the　past　two　decades　because　it　is　important　for　identification.　The　dominant　methods　construct　facial　surface　from　the　soft　tissue　thickness　measured　at　a　set　of　skull　landmarks.　The　quantity　and　position　of　the　landmarks　are　very　vital　for　craniofacial　reconstruction,　but　there　is　no　standard.　In　addition,　it　is　difficult　to　accurately　locate　the　landmarks　on　dense　mesh　without　manual　assistance.　In　this　article,　we　propose　an　automatic　craniofacial　reconstruction　method　based　on　a　hierarchical　dense　deformable　model.　To　construct　the　model,　we　collect　more　than　100　head　samples　by　computerized　tomography　scanner.　The　samples　are　represented　as　dense　triangle　mesh　to　model　face　and　skull　shape.　As　the　deformable　model　demands　all　samples　in　uniform　form,　a　non-rigid　registration　algorithm　is　presented　to　align　the　samples　in　point-to-point　correspondence.　Based　on　the　aligned　samples,　a　global　deformable　model　is　constructed,　and　three　local　models　are　constructed　from　the　segmented　patches　of　the　eye,　nose,　and　mouth.　For　a　given　skull,　the　global　and　local　deformable　models　are　matched　with　it,　and　the　reconstructed　facial　surface　is　obtained　by　fusing　the　global　and　local　reconstruction　results.　To　validate　our　method,　a　face　deformable　model　is　constructed　and　the　reconstruction　results　are　evaluated　in　its　coefficient　domain.　The　experimental　results　indicate　that　the　proposed　method　has　good　performance　for　craniofacial　reconstruction.