In　this　paper,　we　present　a　method　to　generate　novel　realistic　3D　face　model　using　a　model　trained　from　real　3D　face　data.　3D　face　samples　are　an　important　data　platform　for　model　training,　algorithm　design.　Subject　to　the　constraint　of　acquisition　technology,　sizes　of　current　3D　face　databases　are　relatively　small　and　insufficient.　The　presented　method　is　used　to　solve　this　problem.　First　real　3D　face　data　are　collected　as　training　sample　and　a　parametric　global　model　is　learned　based　on　them.　Then　a　local　model　is　established　based　on　surface　quilting.　We　use　the　global　model　to　build　a　novel　coarse　face　model.　Then,　we　condition　the　local　model　with　the　global　model.　With　appropriate　choices　of　local　and　global　models　it　is　possible　to　reliably　generate　novel　realistic　3D　face　data　that　do　not　correspond　to　any　individual　in　the　training　data.　Finally　we　apply　our　model　to　face　recognition　to　examine　the　meaning　of　our　work.　©　2011　ACADEMY　PUBLISHER.