A large-scale study of performance and equity of commercial remote identity verification technologies across demographics
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Abstract (arXiv)
As more types of transactions move online, there is an increasing need to verify someone's identity remotely. Remote identity verification (RIdV) technologies have emerged to fill this need. RIdV solutions typically use a smart device to validate an identity document like a driver's license by comparing a face selfie to the face photo on the document. Recent research has been focused on ensuring that biometric systems work fairly across demographic groups. This study assesses five commercial RIdV solutions for equity across age, gender, race/ethnicity, and skin tone across 3,991 test subjects. This paper employs statistical methods to discern whether the RIdV result across demographic groups is statistically distinguishable. Two of the RIdV solutions were equitable across all demographics, while two RIdV solutions had at least one demographic that was inequitable. For example, the results for one technology had a false negative rate of 10.5% +/- 4.5% and its performance for each demographic category was within the error bounds, and, hence, were equitable. The other technologies saw either poor overall performance or inequitable performance. For one of these, participants of the race Black/African American (B/AA) as well as those with darker skin tones (Monk scale 7/8/9/10) experienced higher false rejections. Finally, one technology demonstrated more favorable but inequitable performance for the Asian American and Pacific Islander (AAPI) demographic. This study confirms that it is necessary to evaluate products across demographic groups to fully understand the performance of remote identity verification technologies.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 33 references that are in the map. First posted on arXiv 18 Sep 2024.
Builds on
9 of 33 references.
References · 9
- 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
- 2020Face Recognition: Too Bias, or Not Too Bias?cited by 82
- 2020Mitigating Face Recognition Bias via Group Adaptive Classifiercited by 67
- 2019Accuracy comparison across face recognition algorithms: Where are we on measuring race bias?cited by 56
- 2020Fairness in Biometrics: a figure of merit to assess biometric verification systemscited by 41
- 2020On the Robustness of Face Recognition Algorithms Against Attacks and Biascited by 16
- 2021Measuring Hidden Bias within Face Recognition via Racial Phenotypescited by 16
- 2021Information-Theoretic Bias Assessment Of Learned Representations Of Pretrained Face Recognitioncited by 5
- 2022Statistical Methods for Assessing Differences in False Non-Match Rates Across Demographic Groupscited by 4
Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025Review of Demographic Fairness in Face Recognitioncited by 7
- 2026Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verificationcited by 1
- 2026Robustness of Presentation Attack Detection in Remote Identity Validation Scenarioscited by 0
- 2026IDSPACE: A Novel Document Generator for Reliable Evaluation of Digital Identity Verification Systems [Extended Technical Report]cited by 0
- 2026IDSPACE: A Novel Document Generator for Reliable Evaluation of Digital Identity Verification Systems [Extended Technical Report]2 Sep 2026
- 2026Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification30 Apr 2026
- 2026Robustness of Presentation Attack Detection in Remote Identity Validation Scenarios26 Jan 2026
- 2025Review of Demographic Fairness in Face Recognition4 Feb 2025