Longitudinal study of fingerprint recognition

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 112; no. 28; pp. 8555 - 8560
• Main Authors: Yoon, Soweon, Anil K. Jain
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 14.07.2015; National Acad Sciences
• Subjects: Accuracy; biometrics; case studies; data collection; Dermatoglyphics; fingerprint recognition; Fingerprinting; Forensic Anthropology; Humans; longitudinal data analysis; Longitudinal Studies; Models, Statistical; multilevel statistical model; persistence of fingerprints; Physical Sciences; Probability; Statistical analysis; Statistical models; Uncertainty; biometrics; longitudinal data analysis; fingerprint recognition; persistence of fingerprints; multilevel statistical model
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1410272112

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). Although 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 10-print records over a minimum time span of 5 y. In regard 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, whereas 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 y, 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 10-finger fusion analysis coincide with the conclusions from single-finger analysis.