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Detecting anisotropy in fingerprint growth
Author(s) -
Markert Karla,
Krehl Karolin,
Gottschlich Carsten,
Huckemann Stephan
Publication year - 2019
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12343
Subject(s) - minutiae , anisotropy , computer science , scanner , parametric statistics , fingerprint (computing) , artificial intelligence , biometrics , computer vision , pattern recognition (psychology) , planar , fingerprint recognition , algorithm , mathematics , statistics , optics , physics , computer graphics (images)
Summary 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 modelling the rate of anisotropy in fingerprint growth facilitate 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 detect statistically anisotropy in planar shape and its preferred axis. For this we develop a new anisotropic growth model with a Procrustes‐type algorithm and a new parametric and non‐parametric neighbourhood hypothesis test, tunable to measurement accuracy. In application to fingerprint growth, we require only a standard fingerprint scanner and a minutiae matcher. 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%. Our method is well applicable to future data sets of child fingerprint time series. We provide an implementation of our algorithms and tests with matched minutiae pattern data.