The　powerful　image　feature　extraction　ability　of　convolutional　neural　network　makes　it　possible　to　achieve　great　success　in　the　field　of　face　recognition.　However,　this　category　of　models　tend　to　be　deep　and　paralleled　which　is　not　capable　to　be　applied　in　real-time　face　recognition　tasks.　In　order　to　improve　its　feasibility,　we　propose　a　max-feature-map　activation　based　fully　convolutional　structure　to　extract　face　features　with　higher　speed　and　less　computational　cost.　The　learned　model　has　a　great　potential　on　embedding　in　the　hardware　devices　due　to　its　high　recognition　performance　and　small　storage　space.　Experimental　results　demonstrate　that　the　proposed　model　is　63　times　smaller　in　comparison　with　the　famous　VGG　model.　At　the　same　time,　96.80%　verification　accuracy　is　achieved　for　a　single　network　on　LFW　benchmark.　©　2017,　Springer　International　Publishing　AG.