To　improve　gender　classification　accuracy,　we　propose　a　cross-connected　convolutional　neural　network　(CCNN)　based　on　traditional　convolutional　neural　networks　(CNN).　The　proposed　model　is　a　9-layer　structure　composed　of　an　input　layer,　six　hidden　layers　(i.e.,　three　convolutional　layers　alternating　with　three　pooling　layers),　a　fully-connected　layer　and　an　output　layer,　where　the　second　pooling　layer　is　allowed　to　directly　connect　to　the　fully-connected　layer　across　two　layers.　Experimental　results　in　ten　face　datasets　show　that　our　model　can　achieve　gender　classification　accuracies　not　lower　than　those　of　the　convolutional　neural　networks.　Copyright　©　2016　Acta　Automatica　Sinica.　All　rights　reserved.