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Detection of false positives by human reviewers depends on the face recognition algorithm and its security configuration

Summary

A new study measured the ability of 801 human reviewers to detect false positives from four commercial face recognition algorithms across three security configurations using the Difficult Facial Image Comparison Test (DFICT). It found that while human reviewers can reduce algorithmic false positives, their performance declines at stricter security thresholds and varies by algorithm, suggesting some algorithms are more compatible with human oversight.

SourcearXiv Computer VisionAuthor: Esha Edakoth, John J. Howard, Isabelle M. Shuggi, Yevgeniy B. Sirotin, Robert Wood
Detection of false positives by human reviewers depends on the face recognition algorithm and its security configuration
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[Submitted on 16 Oct 2026]

Title:Detection of false positives by human reviewers depends on the face recognition algorithm and its security configuration

View a PDF of the paper titled Detection of false positives by human reviewers depends on the face recognition algorithm and its security configuration, by Esha Edakoth and 4 other authors

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Abstract:Integration of artificial intelligence (AI) technology into identity workflows, such as automated face recognition (FR), has enhanced operational capabilities, with FR algorithms achieving super-human scale and accuracy. However, these gains can make human oversight of FR more difficult, raising questions about the effectiveness of these human-AI teams. We measured human reviewers' ability to detect false positive errors made by commercial FR algorithms across varying security configurations. Using a novel dataset, the Difficult Facial Image Comparison Test (DFICT), derived from false positives made by four comparable commercial face recognition algorithms, we measured the ability of 801human reviewers to detect algorithmic false positives across three security configurations, representing operationally relevant biometric thresholds. Our findings show that human reviewers can reduce algorithmic false positives, but performance declines at stricter security thresholds. Further, reviewer performance varied by algorithm indicating that some algorithms are more compatible with human oversight. We suggest that biometric systems should be designed to account for human reviewers' variable effectiveness, particularly in high-security applications. Our findings should be considered during algorithm selection and inform human oversight strategies to minimize AI-related risks in operational FR deployments.

Comments: 26 pages, 8 figures, 4 tables

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)

Cite as: arXiv:2610.33045 [cs.CV]

(or arXiv:2610.33045v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.33045

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yevgeniy Sirotin [view email] [v1] Fri, 16 Oct 2026 18:19:10 UTC (1,130 KB)

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Key points and analysis

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Key points

  • Human reviewers can reduce face recognition false positives, but their detection performance declines at stricter security thresholds.
  • Reviewer performance varies by algorithm, indicating that some algorithms are more compatible with human oversight.
  • The study recommends designing biometric systems to account for variable human reviewer effectiveness, especially in high-security applications.
  • Algorithm selection and human oversight strategies should be informed by these findings to minimize AI-related risks in operational deployments.

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