中文摘要：

为提高性别分类准确率,在传统卷积神经网络（Convolutional neural network,CNN）的基础上,提出一个跨连卷积神经网络（Cross-connected CNN,CCNN）模型.该模型是一个9层的网络结构,包含输入层、6个由卷积层和池化层交错构成的隐含层、全连接层和输出层,其中允许第2个池化层跨过两个层直接与全连接层相连接.在10个人脸数据集上的性别分类实验结果表明,跨连卷积网络的准确率均不低于传统卷积网络.

英文摘要：

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.