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Sensors and Materials, Volume 32, Number 10(1) (2020)
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pp. 3137-3155
S&M2329 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2771
Published: October 9, 2020

Uniform Experimental Design for Optimizing the Parameters of Multi-input Convolutional Neural Networks [PDF]

Cheng-Jian Lin, Chen-Hsien Wu, Chi-Chia Sun, and Cheng-Hsien Lin

(Received January 17, 2020; Accepted May 25, 2020)

Keywords: image recognition, gender classification, convolutional neural network, uniform experimental design

In this paper, a multi-input convolutional neural network (CNN) based on a uniform experimental design (UED) is proposed for gender classification applications. The proposed multi-input CNN uses multiple CNNs to obtain output results through individual training and concatenation. In addition, to avoid using trial and error for determining the architecture parameters of the multi-input CNN, a UED was used in this study. The experimental results confirmed that the dual-input CNN with a UED achieved accuracies of 99.68 and 99.06% for the CIA and MORPH datasets, respectively. The accuracy of the proposed CNN increased significantly when increasing the number of inputs.

Corresponding author: Cheng-Jian Lin

This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Cheng-Jian Lin, Chen-Hsien Wu, Chi-Chia Sun, and Cheng-Hsien Lin, Uniform Experimental Design for Optimizing the Parameters of Multi-input Convolutional Neural Networks, Sens. Mater., Vol. 32, No. 10, 2020, p. 3137-3155.