Human identification by fingerprints is based on the fundamental premise that ridge patterns from distinct fingers are different (uniqueness) and a fingerprint pattern does not change over time (persistence). While the uniqueness of fingerprints has been investigated by developing statistical models to estimate the probability of error in comparing two random samples of fingerprints, the persistence of fingerprints has remained a general belief based on only a few case studies. In this study, fingerprint match (similarity) scores are analyzed by multilevel statistical models with covariates such as time interval between two fingerprints in comparison, subject's age, and fingerprint image quality. Longitudinal fingerprint records of 15,597 subjects are sampled from an operational fingerprint database such that each individual has at least five tenprint records over a minimum time span of 5 years. In regards to the persistence of fingerprints, the longitudinal analysis on a single (right index) finger demonstrates that (i) genuine match scores tend to significantly decrease when time interval between two fingerprints in comparison increases, while the change in impostor match scores is negligible, and (ii) fingerprint recognition accuracy at operational settings, nevertheless, tends to be stable as the time interval increases up to 12 years, the maximum time span in the dataset. However, the uncertainty of temporal stability of fingerprint recognition accuracy becomes substantially large if either of the two fingerprints being compared is of poor quality. The conclusions drawn from ten-finger fusion analysis coincide with the conclusions from single-finger analysis.
Proceedings of the National Academy of Sciences of the United States of America
and Jain, A.
Longitudinal Study of Fingerprint Recognition, Proceedings of the National Academy of Sciences of the United States of America, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=918098
(Accessed December 9, 2023)