{
  "abstract": "Facial recognition technology (FRT) is increasingly used in criminal investigations, yet most evaluations of its accuracy rely on high-quality images, unlike those often encountered by law enforcement. This study examines how five common forms of image degradation (contrast, brightness, motion blur, pose shift, and resolution) affect FRT accuracy, and how that varies by gender and race. Using synthetic faces generated by StyleGAN3 and labeled with FairFace, we simulate degraded images and evaluate performance using the DeepFace framework with the ArcFace model in 1:n identification tasks. By using simulated faces, we are able to estimate conditional FPIR and FNIR error-rate curves across degradation levels under controlled conditions, providing practical guidance on how database search performance changes as questioned image quality deteriorates. We find that high motion blur and contrast and low resolution and brightness substantially increase FNIR. At the selected operating point, FPIR is substantially higher than FNIR and is highest near baseline image quality, generally decreasing as image degradation increases and demographic disparities persist across degradation conditions. Demographic disparities vary by error type: FPIR is often higher for Black individuals, particularly Black women, whereas the demographic groups with the highest FNIR differ across degradation conditions. These disparities raise concerns about fairness and reliability of the use of FRT. These findings highlight the importance of evaluating algorithmic performance using the low-quality images encountered in criminal investigations to ensure both fairness and validity.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Criminology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.",
    "Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.",
    "University of Pennsylvania, School of Engineering and Applied Science, Philadelphia, Pennsylvania, United States of America."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Maria Cuellar",
      "orcid": "0000-0001-7242-9453"
    },
    {
      "affs": [
        0
      ],
      "name": "Hon Kiu James To"
    },
    {
      "affs": [
        2
      ],
      "name": "Arush Mehrotra",
      "orcid": "0009-0008-2050-5600"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/17nr0byjswfv2.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0353793",
  "fulltext_url": "https://europepmc.org/articles/PMC13686280",
  "id": "17nr0byjswfv2",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0353793",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Face recognition and analysis"
    ],
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      "plos",
      "/Biology and life sciences/Anatomy/Head/Face"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognition/Memory/Face recognition"
    ],
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      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Perception/Face recognition"
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      "plos",
      "/Biology and life sciences/Neuroscience/Learning and memory/Memory/Face recognition"
    ],
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      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Perception/Face recognition"
    ],
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      "plos",
      "/Medicine and health sciences/Anatomy/Head/Face"
    ],
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      "plos",
      "/People and places/Population groupings/Professions/Police"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms"
    ],
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      "plos",
      "/Research and analysis methods/Database and informatics methods/Database searching"
    ],
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      "plos",
      "/Research and analysis methods/Imaging techniques"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms"
    ],
    [
      "plos",
      "/Social sciences/Law and legal sciences/Criminal justice system/Criminal investigations"
    ],
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      "plos",
      "/Social sciences/Law and legal sciences/Criminal justice system/Law enforcement"
    ],
    [
      "plos",
      "/Social sciences/Law and legal sciences/Criminal justice system/Law enforcement/Police"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Perception/Face recognition"
    ]
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  "openalex": "W7223147017",
  "pdf_url": "https://europepmc.org/articles/PMC13686280?pdf=render",
  "pmcid": "PMC13686280",
  "pmid": "42905744",
  "published": "2026-10-21",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/17nr0byjswfv2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0353793",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0353793"
    },
    {
      "first_seen": "2026-10-30T12:19:53Z",
      "native_id": "MED:42905744",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42905744"
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  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Accuracy and fairness of facial recognition technology in real-world, low-quality police images: An experiment",
  "title_text": "Accuracy and fairness of facial recognition technology in real-world, low-quality police images: An experiment",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:53Z",
  "url": "https://research.draconitic.com/paper/17nr0byjswfv2",
  "venue": "PLOS One",
  "versions": []
}