Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition

• Main Authors: Cheng-Jian Lin, Cheng-Hsien Lin, Shiou-Yun Jeng
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Applied Sciences
• Subjects: convolutional neural network; gender classification; feature fusion; uniform experimental design; AlexNet
• Online Access: https://www.mdpi.com/2076-3417/10/9/3166

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.