Compared　with　the　traditional　statistical　models,　such　as　the　active　shape　model　and　the　active　appearance　model,　the　facial　feature　point　localization　method　based　on　deep　learning　has　improved　in　accuracy　and　speed,　but　there　still　exist　some　problems.　First,　when　the　traditional　deep　neural　network　model　targets　a　data　set　containing　different　face　poses,　it　only　performs　the　preprocessing　through　the　initialized　face　alignment,　and　does　not　consider　the　regularity　of　the　distribution　of　the　feature　points　corresponding　to　the　face　pose　during　feature　extraction.　Secondly,　the　traditional　deep　neural　network　model　does　not　take　into　account　the　feature　space　differences　caused　by　the　different　position　distribution　of　the　external　contour　points　and　internal　organ　points　(such　as　eyes,　nose　and　mouth),　resulting　in　inconsistent　detection　accuracy　and　difficulty　of　different　feature　points.　In　order　to　solve　the　above　problems　this　paper　proposes　a　convolutional　neural　network　(CNN)　based　on　grayedge-HOG　(GEH)　fusion　feature.　©　2018　The　Authors,　published　by　EDP　Sciences.