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Chinese Conference on Biometric Recognition

CCBR 2019: Biometric Recognition pp 493–501Cite as

Similarity Scores Based Re-classification for Open-Set Person Re-identification

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11818))

Abstract

In this paper, we propose a new similarity scores based re-classification method for open-set person re-identification, which exploits information among the top-n most similar matching candidates in the gallery set. Moreover, to make the cross-view quadratic discriminant analysis metric learning method effectively learn both the projection matrix and the metric kernel with open-set data, we introduce an additional regularization factor to adjust the covariance matrix of the obtained subspace. Our Experiments on challenging OPeRID v1.0 database show that our approach improves the Rank-1 recognition rates at 1% FAR by 8.86% and 10.51% with re-ranking, respectively.

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Acknowlegements

This work was supported by the Chinese National Natural Science Foundation Projects #61806203, #61876178 and #61672521.

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Authors and Affiliations

  1. CBSR&NLPR, Institute of Automation Chinese Academy of Sciences, Beijing, 100190, China

    Hongsheng Wang, Yang Yang, Dong Cao & Zhen Lei

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Hongsheng Wang, Yang Yang, Dong Cao & Zhen Lei

  3. Inception Institute of Artificial Intelligence, Abu Dhabi, UAE

    Shengcai Liao

Authors
  1. Hongsheng Wang
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  2. Yang Yang
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  3. Shengcai Liao
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  4. Dong Cao
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  5. Zhen Lei
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Corresponding author

Correspondence to Zhen Lei .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  2. Chinese Academy of Sciences, Beijing, China

    Ran He

  3. Tsinghua University, Beijing, China

    Jianjiang Feng

  4. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  5. Tsinghua University, Shenzhen, China

    Zhenhua Guo

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Wang, H., Yang, Y., Liao, S., Cao, D., Lei, Z. (2019). Similarity Scores Based Re-classification for Open-Set Person Re-identification. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_54

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  • DOI: https://doi.org/10.1007/978-3-030-31456-9_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31455-2

  • Online ISBN: 978-3-030-31456-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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