A study on quantifying the individuality of fingerprints and the <scp>3D</scp> feature distribution of minutiae

The hypothesis of fingerprint individuality continues to be debated due to limited empirical verification, impacting the scientific foundation of fingerprint identification. This study proposed a quantitative model for fingerprint individuality and investigated the three-dimensional (3D) distribution of minutiae. This model considered the position and direction of minutiae as 3D feature variables. We extracted 3D feature data from 56,812,114 known fingerprints based on the automatic fingerprint identification system (AFIS). Following data calibration, translation, and error correction, we statistically analyzed the distribution density of minutiae. We developed the algorithm to calculate the individuality score of a single fingerprint through the individuality model. The experimental results showed that the minutiae distribution followed distinct patterns. The distribution density of minutiae exhibited symmetry between corresponding fingers on left/right hands. Significant variations in minutiae distribution density and central point distribution were observed across the five pattern types (whorl, left loop, right loop, arch, accidental). Minutiae with different directions exhibited symmetry along the Y-axis in both positional and quantitative distribution. Minutiae within diagonally opposite angular ranges showed similar distribution trends. The individuality scores were robust to distinguish different fingerprints. We preliminarily applied the individuality score to provide a basis for modifying the AFIS scoring mechanism, and we found that the individuality score of same-source fingerprints was greater than that of close nonmatches (CNMs). This work provides novel insights into fingerprint individuality and establishes a statistical foundation for refining AFIS scoring mechanisms and likelihood-ratio evidence evaluation frameworks.