On Demographic Bias in Fingerprint Recognition
- URL: http://arxiv.org/abs/2205.09318v1
- Date: Thu, 19 May 2022 04:10:59 GMT
- Title: On Demographic Bias in Fingerprint Recognition
- Authors: Akash Godbole, Steven A. Grosz, Karthik Nandakumar, Anil K. Jain
- Abstract summary: We propose a formal statistical framework to test for the existence of bias in fingerprint recognition across four major demographic groups.
Experiments on two different fingerprint databases show that demographic differentials in SOTA fingerprint recognition systems decrease as the matcher accuracy increases.
- Score: 37.44111989759035
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fingerprint recognition systems have been deployed globally in numerous
applications including personal devices, forensics, law enforcement, banking,
and national identity systems. For these systems to be socially acceptable and
trustworthy, it is critical that they perform equally well across different
demographic groups. In this work, we propose a formal statistical framework to
test for the existence of bias (demographic differentials) in fingerprint
recognition across four major demographic groups (white male, white female,
black male, and black female) for two state-of-the-art (SOTA) fingerprint
matchers operating in verification and identification modes. Experiments on two
different fingerprint databases (with 15,468 and 1,014 subjects) show that
demographic differentials in SOTA fingerprint recognition systems decrease as
the matcher accuracy increases and any small bias that may be evident is likely
due to certain outlier, low-quality fingerprint images.
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