The　limited　resolution　of　cameras　and　the　wide　field　of　the　video　surveillance　systems　lead　to　low　quality　captured　facial　images　and　difficult　to　identify.　Face　super-resolution　methods　are　proposed　to　enhance　the　resolution　of　facial　images.　However,　it　remains　a　challenging　issue　to　restore　discriminative　features　to　identify　a　specific　person　in　surveillance　videos.　An　algorithm　that　helps　face　super-resolution　and　recognition　with　the　aid　of　discriminative-attributes　is　proposed　in　this　paper.　We　introduce　discriminative-attributes　for　face　recognition　to　recover　discriminative　features　in　the　reconstructed　facial　images.　Attributes　with　more　discriminative　power　are　selected　to　input　the　network　together　with　the　low-resolution　face　image.　The　experimental　results　of　the　LFW-a　benchmark　test　show　that　our　method　achieves　promising　results　in　both　subjective　visual　quality　and　face　recognition　accuracy.　©　Springer　Nature　Switzerland　AG　2019.