Kinship Verification Based on Deep Convolutional Neural Network End-to-End Model
HU Zhengping1, GUO Zengjie1, WANG Meng1, SUN Degang2, REN Dawei1
1.Hebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinghuangdao 066004 2.School of Electronic Information Engineering, Shandong Huayu University of Technology, Dezhou 253000
Abstract：An algorithm based on deep convolutional neural network end-to-end model is proposed to solve the problem of kinship verification with facial image. Firstly, a deep convolutional neural network model is constructed. It consists of convolutional layers, fully connected layer and soft-max classification layer.The implicit features of parent-child images can be extracted by convolution layers. Then, the extracted latent features can be mapped into two-class classification problem of kin verification by fully connected layer, and the kinship relationship of samples can be directly determined by the soft-max classifier. Then, the paired tag training data are inputted into the network to be iterated and parameters of the deep network model are optimized until the loss curve is stable. Finally, the input image pairs are classified by the trained parameters, and the final accuracy is obtained by statistics. The experimental results on the KinFaceW-I database and KinFaceW-II dataset demonstrate the proposed convolutional neural network end-to-end model outperforms other kinship verification algorithms.
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2018, Vol. 31 
