Face　liveness　detection　is　a　significant　research　topic　in　face-based　online　authentication.　The　current　face　liveness　detection　approaches　utilize　either　static　or　dynamic　features,　but　not　both.　In　fact,　the　dynamic　and　static　features　have　different　advantages　in　face　liveness　detection.　In　this　paper,　we　propose　a　scheme　combining　dynamic　and　static　features　to　capture　merits　of　them　for　face　liveness　detection.　First,　the　dynamic　maps　are　captured　from　the　inter-frame　motion　in　the　video,　which　investigates　motion　information　of　the　face　in　the　video.　Then,　with　a　Convolutional　Neural　Network　(CNN),　the　dynamic　and　static　features　are　extracted　from　the　dynamic　maps　and　the　frame　images,　respectively.　Next,　in　CNN,　the　fully　connected　layers　containing　the　dynamic　and　static　features　are　concatenated　to　form　a　fused　feature.　Finally,　the　fused　features　are　used　to　train　a　binary　Support　Vector　Machine　(SVM)　classifier,　which　classifies　the　frames　into　two　categories,　i.e.　frame　with　real　or　fake　face.　Experimental　results　and　the　corresponding　analysis　demonstrate　that　the　proposed　scheme　is　capable　of　discovering　face　liveness　by　fusing　dynamic　and　static　features　and　it　outperforms　the　current　state-of-the-art　face　liveness　detection　approaches.