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Effectiveness of Blind Face Restoration to Boost Face Recognition Performance at Low-Resolution Images

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Progress in Artificial Intelligence and Pattern Recognition (IWAIPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14335))

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Abstract

This paper studies the effectiveness of Blind Face Restoration methods to boost the performance of face recognition systems on low-resolution images. We investigate the use of three blind face restoration techniques, which have demonstrated impressive results in generating realistic high-resolution face images. Three state-of-the-art face recognition methods were selected to assess the impact of using the generated high-resolution images on their performance. Our analysis includes both, synthesized and native low-resolution images. The conducted experimental evaluation show that this is still an open research problem.

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Notes

  1. 1.

    https://github.com/yangxy/GPEN.

  2. 2.

    https://github.com/TencentARC/GFPGAN/.

  3. 3.

    https://shangchenzhou.com/projects/CodeFormer/.

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Author information

Authors and Affiliations

  1. Advanced Technology Application Center (CENATAV), 7A #21406, Havana, Cuba

    Yoanna Martínez-Díaz & Heydi Méndez-Vázquez

  2. Tecnológico de Monterrey, School of Engineering and Science, Monterrey, Mexico

    Luis S. Luévano

Authors
  1. Yoanna Martínez-Díaz
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  2. Luis S. Luévano
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  3. Heydi Méndez-Vázquez
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Corresponding author

Correspondence to Heydi Méndez-Vázquez .

Editor information

Editors and Affiliations

  1. Universidad de las Ciencias Informáticas, Havana, Cuba

    Yanio Hernández Heredia

  2. Universidad de las Ciencias Informáticas, Havana, Cuba

    Vladimir Milián Núñez

  3. Universidad de las Ciencias Informáticas, Havana, Cuba

    José Ruiz Shulcloper

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Martínez-Díaz, Y., Luévano, L.S., Méndez-Vázquez, H. (2024). Effectiveness of Blind Face Restoration to Boost Face Recognition Performance at Low-Resolution Images. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2023. Lecture Notes in Computer Science, vol 14335. Springer, Cham. https://doi.org/10.1007/978-3-031-49552-6_39

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  • DOI: https://doi.org/10.1007/978-3-031-49552-6_39

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