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Using PGAN to Create Synthetic Face Images to Reduce Bias in Biometric Systems

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Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13645))

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Abstract

This work does not aim to advance the state of the art for face demographic classification systems, but rather to show how synthetic images can help tackle demographic unbalance in training them. The problem of demographic bias in both face recognition and face analysis has often been underlined in recent literature, with controversial experimental results. The outcomes presented here both confirm the advantage of using synthetic face images to add samples to under-represented classes and suggest that the achieved performance increase is proportional to the starting unbalance.

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Notes

  1. 1.

    https://gdpr-info.eu/.

  2. 2.

    https://susanqq.github.io/UTKFace/.

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Authors and Affiliations

  1. Department of Computer Science, Sapienza University of Rome, Rome, Italy

    Andrea Bozzitelli, Pia Cavasinni di Benedetto & Maria De Marsico

Authors
  1. Andrea Bozzitelli
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  2. Pia Cavasinni di Benedetto
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  3. Maria De Marsico
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Corresponding author

Correspondence to Maria De Marsico .

Editor information

Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. University of Ontario Institute of Technology, Oshawa, ON, Canada

    Bill Kapralos

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Bozzitelli, A., Cavasinni di Benedetto, P., De Marsico, M. (2023). Using PGAN to Create Synthetic Face Images to Reduce Bias in Biometric Systems. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_39

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  • DOI: https://doi.org/10.1007/978-3-031-37731-0_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37730-3

  • Online ISBN: 978-3-031-37731-0

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