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Joint Generic Learning and Multi-source Domain Adaptation on Unsupervised Face Recognition

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1252))

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Abstract

For most machine learning algorithms, the performance of model trained on the source domain degenerates obviously when it is applied to the target domain because the distributions of both domains are different. Besides, in many cases, there is a lack of adequate supervised target domain training samples. In order to solve the mismatch between the domains and the lack of training samples, this paper proposes jointing domain adaptation and generic learning on solving unsupervised face recognition. Firstly, samples are selected randomly from multiple source and target domains which do not contain interest subjects and construct multiple sub-datasets. Secondly, learning a common subspace for each sub-dataset. In the common subspace, source and target domains can mutual interlace and their structures are well preserved, and we can get more discrimination information from multiple feature subspaces. Finally, the recognition is obtained by using combine strategies. The experimental results show that the recognition performance of the framework is better than that of the competitive ones.

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Acknowledgments

This work is partially supported by The National Natural Science Foundation of China (61662014, 61702130, 61762025), Image intelligent processing project of Key Laboratory Fund (GIIP1804) and Innovation Project of GUET Graduate Education (2018YJCX44).

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Authors and Affiliations

  1. Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, 541004, China

    Zhi Xu, Han Han, Jianqiu Ji & Baohua Qiang

Authors
  1. Zhi Xu
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  2. Han Han
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  3. Jianqiu Ji
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  4. Baohua Qiang
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Corresponding author

Correspondence to Jianqiu Ji .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Xu, Z., Han, H., Ji, J., Qiang, B. (2020). Joint Generic Learning and Multi-source Domain Adaptation on Unsupervised Face Recognition. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1252. Springer, Singapore. https://doi.org/10.1007/978-981-15-8083-3_2

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  • DOI: https://doi.org/10.1007/978-981-15-8083-3_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8082-6

  • Online ISBN: 978-981-15-8083-3

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