3D　face　reconstruction　from　skull　has　been　investigated　deeply　by　computer　scientists　in　the　past　two　decades　because　it　is　important　for　identification.　The　dominant　methods　construct　3D　face　from　the　soft　tissue　thickness　measured　at　a　set　of　landmarks　on　skull.　The　quantity　and　position　of　the　landmarks　are　very　vital　for　3D　face　reconstruction,　but　there　is　no　uniform　standard　for　the　selection　of　the　landmarks.　Additionally,　the　acquirement　of　the　landmarks　on　skull　is　difficult　without　manual　assistance.　In　this　paper,　an　automatic　3D　face　reconstruction　method　based　on　a　hierarchical　dense　deformable　model　is　proposed.　To　construct　the　model,　the　skull　and　face　samples　are　acquired　by　CT　scanner　and　represented　as　dense　triangle　mesh.　Then　a　non-rigid　dense　mesh　registration　algorithm　is　presented　to　align　all　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　globe　and　local　deformable　models　are　iteratively　matched　with　it,　and　the　reconstructed　facial　surface　is　obtained　by　fusing　the　globe　and　local　reconstruction　results.　To　validate　the　presented　method,　a　measurement　in　the　coefficient　domain　of　a　face　deformable　model　is　defined.　The　experimental　results　indicate　that　the　proposed　method　has　good　performance　for　3D　face　reconstruction　from　skull.