Abstract
The analysis of facial information is always an important hotspot issue in the field of computer vision and pattern recognition and kinship verification by facial image is a challenging problem. Facial kinship verification has wide application range and important research value not only in the field of biometrics analysis but also in the social fields, such as analysis of mining social network data, searching work for scattered family members and so on. At present, with the development of computer vision especially the deep learning and metric learning, face recognition has made great achievements in recent years. In this thesis, a framework for kinship verification based on deep learning is proposed. Comparing with the current research methods that focus on metric learning, we use a deep learning network model to replace the two processes of feature extraction and metric learning. The effectiveness of the method is verified in KinFaceW datasets and TSKinFace datasets. The experimental results show that the accuracy is about 91% in KinFaceW datasets, and about 89.5% in TSKinFace datasets.
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Acknowledgements
This research is based on work supported by the Yinfeng Gene Technology Co. Ltd. We thank all families who took part in this study. Thanks to the data shared by researchers on the Internet.
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Zhou, H. et al. (2020). A Method for Facial Kinship Verification Based on Deep Learning. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_17
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DOI: https://doi.org/10.1007/978-981-13-9406-5_17
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