Face　liveness　detection　is　an　interesting　research　topic　in　face-based　online　authentication.　The　current　face　liveness　detection　algorithms　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　discuss　a　scheme　to　combine　dynamic　and　static　features　that　combines　the　strength　of　each.　First,　the　dynamic　maps　are　obtained　from　the　inter　frame　motion　in　the　video.　Then,　using　a　Convolutional　Neural　Network　(CNN),　the　dynamic　and　static　features　are　extracted　from　the　dynamic　maps　and　the　images,　respectively.　Next,　the　fully　connected　layers　from　the　CNN　that　include　the　dynamic　and　static　features　are　connected　to　form　the　fused　features.　Finally,　the　fused　features　are　used　to　train　a　two-value　Support　Vector　Machine　(SVM)　classifier,　which　classify　the　images　into　two　groups,　images　with　real　faces　and　images　with　fake　faces.　We　conduct　experiments　to　assess　our　algorithm　that　includes　classifying　images　from　two　public　databases.　Experimental　results　demonstrate　that　our　algorithm　outperforms　current　state-of-the-art　face　liveness　detection　algorithms.