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Fair Face Recognition Using Data Balancing, Enhancement and Fusion

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Computer Vision – ECCV 2020 Workshops (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12540))

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Abstract

Racial bias is an important issue in biometrics, while has not been thoroughly studied in deep face recognition. By reducing the influence of gender and skin colour, this paper proposes a fair face recognition system with low bias. First, multiple preprocessing methods are added to improve the dual shot face detector for obtaining target face from a given test image. Then, a data re-sampling approach is employed to balance the data distribution and reduce the bias based on the analysis of training data. Moreover, multiple data enhancement methods are used to increase the accuracy performance. Finally, a linear-combination strategy is adopted to benefit from mutil-model fusion. ChaLearn Looking at People Fair Face Recognition challenge is supported by ECCV 2020. Our team (ustc-nelslip) ranked 1st in the development stage and 2nd in the test stage of this challenge. The code is available at https://github.com/HaoSir/ECCV-2020-Fair-Face-Recognition-challenge_2nd_place_solution-ustc-nelslip-.

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Author information

Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Jun Yu, Xinlong Hao & Haonian Xie

  2. School of Computer and Information, Hefei University of Technology, Hefei, China

    Ye Yu

Authors
  1. Jun Yu
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  2. Xinlong Hao
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  3. Haonian Xie
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  4. Ye Yu
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Corresponding author

Correspondence to Xinlong Hao .

Editor information

Editors and Affiliations

  1. University of Clermont Auvergne, Clermont Ferrand, France

    Adrien Bartoli

  2. Università degli Studi di Udine, Udine, Italy

    Andrea Fusiello

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Yu, J., Hao, X., Xie, H., Yu, Y. (2020). Fair Face Recognition Using Data Balancing, Enhancement and Fusion. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12540. Springer, Cham. https://doi.org/10.1007/978-3-030-65414-6_34

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  • DOI: https://doi.org/10.1007/978-3-030-65414-6_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65413-9

  • Online ISBN: 978-3-030-65414-6

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