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Person re-identification by discriminant analytical least squares metric learning

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Abstract

Person re-identification means retrieving a same person in large amounts of images among disjoint camera views. An effective and robust similarity measure between a person image pair plays an important role in the re-identification tasks. In this work, we propose a new metric learning method based on least squares for person re-identification. Specifically, the similar training images pairs are used to learn a linear transformation matrix by being projected to finite discrete discriminant points using regression model; then, the metric matrix can be deduced by solving least squares problem with a closed form solution. We call it discriminant analytical least squares (DALS) metric. In addition, we develop the incremental learning scheme of DALS, which is particularly valuable in model retraining when given additional samples. Furthermore, DALS could be effectively kernelized to further improve the matching performance. Extensive experiments on the VIPeR, GRID, PRID450S and CUHK01 datasets demonstrate the effectiveness and efficiency of our approaches.

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Acknowledgements

This research was supported by the National Natural Science of Foundation of China (Nos. 61501177, 61772455, 61363022, 61572486, U1713213), Guangzhou Key Laboratory (No. 201605030014), Guangzhou University’s training program for excellent newly recruited doctors (No. YB201712), the Yunnan Natural Science Funds (No. 2016FB105), the Program for Excellent Young Talents of Yunnan University (No. WX069051) and the Project of Innovative Research Team of Yunnan Province.

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Authors and Affiliations

  1. School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, 510006, People’s Republic of China

    Zhao Yang & Xiao Hu

  2. School of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming, 650224, People’s Republic of China

    Fei Dai

  3. UBTECH Robotics, Shenzhen, 518000, People’s Republic of China

    Jianxin Pang

  4. School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, 650031, People’s Republic of China

    Tao Jiang

  5. School of Information Science and Engineering, Yunnan University, Kunming, 650091, People’s Republic of China

    Dapeng Tao

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  1. Zhao Yang
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  2. Xiao Hu
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  4. Jianxin Pang
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  5. Tao Jiang
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Correspondence to Dapeng Tao.

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Yang, Z., Hu, X., Dai, F. et al. Person re-identification by discriminant analytical least squares metric learning. Machine Vision and Applications 29, 1019–1031 (2018). https://doi.org/10.1007/s00138-018-0917-z

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  • DOI: https://doi.org/10.1007/s00138-018-0917-z

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