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Article

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

1
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan
2
School of Intelligence, National Taichung University of Science and Technology, Taichung 404, Taiwan
3
Department of Electrical Engineering, National Chung Hsing University, Taichung City 402, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(9), 3166; https://doi.org/10.3390/app10093166
Received: 18 March 2020 / Revised: 21 April 2020 / Accepted: 29 April 2020 / Published: 1 May 2020
(This article belongs to the Special Issue Advances of Computer Vision)
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. View Full-Text
Keywords: convolutional neural network; gender classification; feature fusion; uniform experimental design; AlexNet convolutional neural network; gender classification; feature fusion; uniform experimental design; AlexNet
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MDPI and ACS Style

Lin, C.-J.; Lin, C.-H.; Jeng, S.-Y. Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition. Appl. Sci. 2020, 10, 3166. https://doi.org/10.3390/app10093166

AMA Style

Lin C-J, Lin C-H, Jeng S-Y. Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition. Applied Sciences. 2020; 10(9):3166. https://doi.org/10.3390/app10093166

Chicago/Turabian Style

Lin, Cheng-Jian, Cheng-Hsien Lin, and Shiou-Yun Jeng. 2020. "Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition" Applied Sciences 10, no. 9: 3166. https://doi.org/10.3390/app10093166

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