Uniform Experimental Design for Optimizing the Parameters of Multi-input Convolutional Neural Networks
Cheng‐Jian Lin, Chen-Hsien Wu, Chi‐Chia Sun, Cheng‐Hsien Lin · Sensors and Materials · 2020
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.