Craniofacial　reconstruction　aims　to　estimate　an　individual＇s　facial　appearance　from　its　skull.　It　can　be　applied　in　many　multimedia　services　such　as　forensic　medicine,　archaeology,　face　animation　etc.　In　this　paper,　a　statistical　learning　based　method　is　proposed　for　3D　craniofacial　reconstruction.　In　order　to　well　represent　the　craniofacial　shape　variation　and　better　utilize　the　relevance　between　different　local　regions,　two　tensor　models　are　constructed　for　the　skull　and　the　face　skin　respectively,　and　multi-linear　subspace　analysis　is　used　to　extract　the　craniofacial　subspace　features.　A　partial　least　squares　regression　(PLSR)　based　mapping　from　skull　subspace　to　skin　subspace　is　established　with　the　attributes　such　as　age　and　BMI　being　considered.　For　an　unknown　skull,　the　3D　face　skin　is　reconstructed　using　the　learned　mapping　with　the　help　of　the　skin　tensor　model.　Compared　with　some　other　statistical　learning　based　method　in　literature,　the　proposed　method　more　directly　and　properly　reflects　the　shape　relationship　between　the　skull　and　the　face.　In　addition,　the　proposed　method　has　little　manual　intervention.　Experimental　results　show　that　the　proposed　method　is　valid.