###### Abstract  
In recent years, convolutional neural networks (CNNs) have been successfully used in image recognition and image classification. General CNNs only use a single image as feature extraction. If the quality of the obtained image is not good, it is easy to cause misjudgment or recognition error. Therefore, this study proposes the feature fusion of a dual-input CNN for the application of face gender classification. In order to improve the traditional feature fusion method, this paper also proposes a new feature fusion method, called the weighting fusion method, which can effectively improve the overall accuracy. In addition, in order to avoid the parameters of the traditional CNN being determined by the user, this paper uses a uniform experimental design (UED) instead of the user to set the network parameters. The experimental results show that in the dual-input CNN experiment, average accuracy rates of 99.98% and 99.11% on the CIA and MORPH data sets are achieved, respectively, which is superior to the traditional feature fusion method. Keywords:convolutional neural network; gender classification; feature fusion; uniform experimental design; AlexNet +
Footnote †: journal:  
improve the accuracy when the network reduces the parameters. He et al. [15] proposed the residual structure to directly map the features of the lower layer to the higher-level network--that is, the deeper network has the representation capabilities close to the previous layers. Therefore, in the deep CNN, the difficult trained problem of the network can be effectively solved. In the above-mentioned methods, the convolutional neural network uses only a single input. Therefore, some researchers began to study multi-input convolutional neural networks. Su et al. [16] proposed a multi-view CNN for the classification of 3D models. By shooting 3D models, two-dimensional pictures with different perspectives were taken as network inputs. Sun et al. [17] used a multi-input CNN for flower grading. They used three different flower images as inputs, and fused the features through convolution and pooling operations. Li et al. [18] developed a dual-input neural network architecture for detecting coronary artery disease (CAD). They used Electrocardiogram and Phonocardiogram signals as input of the network to extract different signal characteristics. Two signal characteristics are combined to improve the accuracy of classification. These results prove that multi-input CNNs can effectively improve the classification accuracy, and have a better performance than single-input CNNs. Using multi-input CNNs will provide different features, and the advantage of each network feature to improve the accuracy of the entire system is an extremely important task. How to properly integrate these different features is an important issue, as the individual features obtained by multiple networks have different interpretations of the same image. Some features obtained can allow the network to determine the correct result, and some features obtained can also cause serious misjudgment. In order to solve this problem, a multi-layer network fusion mechanism [19] is added to the output of a feature network, which partially enhances or suppresses each of the original output features to perform a fusion operation. Thus, multiple features can cooperate with each other and improve the overall recognition rate. In multi-input CNNs, feature fusion techniques such as summation operation [20], product operation [21], maximum operation [22] and concatenation operation [23] are often used. Feature fusion is the integration of multiple different feature information in order to obtain more prominent feature information. Different feature fusion methods will produce different performances. Choosing a reasonable fusion method has important value for improving accuracy. In the above-mentioned networks, the parameters designed by the user are not the optimal parameters of CNNs.