Centrality of the Fingerprint Core Location

TL;DR

This study systematically characterizes the empirical distribution of the fingerprint core across large datasets of rolled and plain fingerprints, explicitly addressing incomplete rolling and core centrality. It implements a multi-step pipeline combining normality testing, information-criterion-based distribution selection, and generalized Monte Carlo goodness-of-fit to identify suitable models, revealing a nonzero core offset from the image center and finger-dependent variability. The results show that rolled cores are best described by a non-central Fischer distribution for horizontal positions, with substantial but finite centrality scatter ($caro_{core}^x \approx 6\%$--$8\%$), and that a vertical offset correlates modestly with NFIQ 2 quality, suggesting practical implications for pose correction and synthetic fingerprint generation. Overall, the work provides a quantitative foundation for using the fingerprint core as a robust landmark in both traditional and contactless biometric workflows and highlights the role of data quality metrics in interpreting core position.

Abstract

Fingerprints have long been recognized as a unique and reliable means of personal identification. Central to the analysis and enhancement of fingerprints is the concept of the fingerprint core. Although the location of the core is used in many applications, to the best of our knowledge, this study is the first to investigate the empirical distribution of the core over a large, combined dataset of rolled, as well as plain fingerprint recordings. We identify and investigate the extent of incomplete rolling during the rolled fingerprint acquisition and investigate the centrality of the core. After correcting for the incomplete rolling, we find that the core deviates from the fingerprint center by 5.7% $\pm$ 5.2% to 7.6% $\pm$ 6.9%, depending on the finger. Additionally, we find that the assumption of normal distribution of the core position of plain fingerprint recordings cannot be rejected, but for rolled ones it can. Therefore, we use a multi-step process to find the distribution of the rolled fingerprint recordings. The process consists of an Anderson-Darling normality test, the Bayesian Information Criterion to reduce the number of possible candidate distributions and finally a Generalized Monte Carlo goodness-of-fit procedure to find the best fitting distribution. We find the non-central Fischer distribution best describes the cores' horizontal positions. Finally, we investigate the correlation between mean core position offset and the NFIQ 2 score and find that the NFIQ 2 prefers rolled fingerprint recordings where the core sits slightly below the fingerprint center.

Figures (2)

• Figure 1: Fitted distributions for the left ring finger (FGP 9).
• Figure 2: Correlations between the Mean Core Offset in X and Y direction and the NFIQ 2 score for rolled (blue) and plain (orange) fingerprint recordings, for each finger position (see markers in legend).