基于深度卷积神经网络End-to-End模型的亲属关系认证算法

doi:10.16451/j.cnki.issn1003-6059.201806008

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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.

Received: 30 November 2017

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61071199), Natural Science Foundation of Hebei Province(No.F2016203422)

About author:: (HU Zhengping(Corresponding author), Ph.D., professor. His research interests include pattern recognition and its applications.)(GUO Zengjie, master student. His research interests include image processing and pattern recognition.)(WANG Meng, Ph.D. candidate. Her research interests include pattern recognition.)(SUN Degang, master student. His research interests include software engineering.)(REN Dawei, master, senior engineer. His research interests include pattern recognition and information processing.)

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Cite this article:

HU Zhengping,GUO Zengjie,WANG Meng等. Kinship Verification Based on Deep Convolutional Neural Network End-to-End Model[J]. , 2018, 31(6): 554-561.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201806008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I6/554

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