A　new　facial　feature　extraction　method　is　proposed　in　this　paper.　Based　on　linear　combination　model,　the　method　locates　feature　points　in　facial　images　precisely.　The　model　uses　the　knowledge　of　prototypic　faces　to　interpret　novel　faces.　To　get　the　knowledge,　the　prototypes　are　labeled　manually　on　the　feature　points.　Generally,　the　construction　of　the　linear　combination　model　depends　on　pixel-wise　alignments　of　prototypes,　and　the　alignments　are　computed　by　an　optical,flow　algorithm　or　bootstrapping　algorithm　which　is　a　full-scale　optimization　and　not　includes　local　information　such　as　facial　feature　points.　To　combine　local　facial　feature　with　the　linear　combination　model,　a　restrained　optical　flow　algorithm　is　proposed　to　compute　the　pixel-wise　alignments.　With　the　information　of　labeled　feature　points,　the　model　matches　the　input　facial　images　and　extracts　the　feature　points　automatically.　Implementing　the　feature　extraction　method　on　the　MPI　face　database,　the　experimental　results　show　that　the　method　has　good　performance.