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.
Highlights
In recent years, the rapid rise of deep learning methods has become the most popular research topic.Deep learning methods have been widely used in classification [1,2,3], identification [4,5,6], and target segmentation [7,8,9]
Deep learning methods are superior to traditional image processing methods, as they do not require the user to determine the capture of image features
Two signal characteristics are combined to improve the accuracy of classification. These results prove that multi-input convolutional neural networks (CNNs) can effectively improve the classification accuracy, and have a better performance than single-input CNNs
Summary
The rapid rise of deep learning methods has become the most popular research topic.Deep learning methods have been widely used in classification [1,2,3], identification [4,5,6], and target segmentation [7,8,9]. Deep learning methods are superior to traditional image processing methods, as they do not require the user to determine the capture of image features. They can extract features in images through self-learning of convolutional and pooling layers in a network. The most typical example is the feature learning and recognition through the convolutional neural network (CNN). LeCun et al proposed the first CNN architecture, LeNet-5 [10], and applied this network to the handwriting recognition in the MNIST dataset. The recognition accuracy of LeNet-5 is better than those of other traditional image processing methods. Krizhevsky et al [11] proposed
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