Detecting Anisotropy in Fingerprint Growth

TL;DR

This paper introduces a statistical tool chain to detect and model anisotropic growth in fingerprints, facilitating long-term biometric identification despite distortions from standard scanners.

Contribution

We develop a novel perturbation model and Procrustes-type algorithm to statistically detect fingerprint growth anisotropy and its axis using standard scanners and minutiae matching.

Findings

Detects anisotropic growth with small sample sizes

Identifies growth differences exceeding 5%

Applicable to children's fingerprint datasets

Abstract

From infancy to adulthood, human growth is anisotropic, much more along the proximal-distal axis (height) than along the medial-lateral axis (width), particularly at extremities. Detecting and modeling the rate of anisotropy in fingerprint growth, and possibly other growth patterns as well, facilitates the use of children's fingerprints for long-term biometric identification. Using standard fingerprint scanners, anisotropic growth is highly overshadowed by the varying distortions created by each imprint, and it seems that this difficulty has hampered to date the development of suitable methods, detecting anisotropy, let alone, designing models. We provide a tool chain to statistically detect, with a given confidence, anisotropic growth in fingerprints and its preferred axis, where we only require a standard fingerprint scanner and a minutiae matcher. We build on a perturbation model, a new Procrustes-type algorithm, use and develop several parametric and non-parametric tests for different hypotheses, in particular for neighborhood hypotheses to detect the axis of anisotropy, where the latter tests are tunable to measurement accuracy. Taking into account realistic distortions caused by pressing fingers on scanners, our simulations based on real data indicate that, for example, already in rather small samples (56 matches) we can significantly detect proximal-distal growth if it exceeds medial-lateral growth by only around 5 percent. Our method is well applicable to future datasets of children fingerprint time series and we provide an implementation of our algorithms and tests with matched minutiae pattern data.