As　a　widely　mentioned　topic　in　face　recognition,　the　margin-based　loss　function　enhances　the　discriminability　of　face　recognition　models　by　applying　margin　between　class　decision　boundaries.　However,　there　is　still　room　to　improve　the　representation　of　face　features.　Local　face　feature　extraction　has　been　employed　in　traditional　face　recognition　methods,　but　with　the　increase　of　network　depth　in　deep　learning,　the　traditional　approach　requires　a　large　number　of　computational　resources.　In　this　paper,　we　propose　a　novel　face　recognition　architecture　called　LocalFace　to　extract　local　face　features.　First,　by　analyzing　the　distribution　of　significant　features　in　face　images,　we　propose　an　efficient　face　fixed-point　local　feature　extraction　approach　and　improve　this　method　to　propose　a　more　effective　face　dynamic　local　feature　extraction　scheme.　Subsequently,　we　propose　a　block-based　random　occlusion　method　for　the　limitations　of　the　random　face　occlusion　method　to　better　simulate　the　occlusion　situation　in　real　scenes.　In　the　end,　we　present　a　detailed　discussion　on　the　channel　attention　method　that　is　more　appropriate　for　face　recognition　and　classification　tasks.　Our　method　enhances　the　representation　of　face　features　by　ensembling　local　features　into　global　features　without　extra　parameters,　which　is　efficient　and　easy　to　implement.　Extensive　experiments　on　various　benchmarks　demonstrate　the　superiority　of　our　LocalFace,　and　part　of　the　experimental　results　achieve　SOTA　results.　©　2022　Elsevier　B.V.