A　convolutional　neural　network　(CNN)　can　perform　well　in　a　variety　of　applications　such　as　human　face　gender　classification,　but　requiring　flips　of　convolutional　kernels　in　implementation.　By　replacing　convolution　with　correlation,　we　propose　a　correlational　neural　network　(CorNN)　instead　of　a　CNN.　A　CorNN　takes　advantage　over　a　CNN　in　that　it　requires　no　flips　of　correlational　kernels　in　implementation,　saving　a　lot　of　training　and　testing　time.　Experimental　results　show　that　an　8-layer　CorNN　for　gender　classification　can　not　only　perform　as　well　as　the　corresponding　CNN,　but　also　run　surprisingly　faster　with　a　relative　reduction　of　11.29%similar　to　18.83%　training　time,　and　10.16%similar　to　16.57%　testing　time.