Infant fingerprint recognition improved by tuning quality assessment methods
Infant Fingerprints: Quality Assessment & Recognition
Computer Vision and Pattern Recognition
Summary
Infant fingerprints are harder to read than adult ones because babies have smaller and less clear fingerprint patterns, and they move a lot when captured. The authors tested ten methods for checking fingerprint quality on babies from birth to three months old. They adjusted these methods to better fit baby fingerprints and found that this tuning helped improve fingerprint recognition. Using these improved settings made it easier and more reliable to identify infants by their fingerprints.
What this means in practice
- •For biometric system developers: Improve infant fingerprint recognition by recalibrating quality assessment methods based on age-specific characteristics from newborn to three months old.
- •For digital identity managers: Increase reliability in infant identification by using tuned fingerprint quality measures to guide fingerprint gallery selections and recognition processes.
Authors
João L. H. Dall Agnol, Dalcimar Casanova, Rodrigo Minetto, Luiz F. P. Southier, Marcelo Teixeira, Marcelo Filipak, Anil K. Jain
Abstract
Infant fingerprint recognition is hindered by a combination of physiological characteristics, such as small ridge width and spacing, and acquisition challenges that include involuntary motion, skin residue, and uneven fingertip pressure. These factors undermine fingerprint quality standards originally designed for adults, which assume feature distributions that do not hold for infants. To address this gap, this paper evaluates ten fingerprint quality assessment methods on a longitudinal dataset that spans six age groups from newborns to three months, alongside a parameter tuning routine that uncovers age-specific fingerprint characteristics to recalibrate methods with domain-dependent assumptions. Both default and tuned configurations are compared through recognition experiments assessing the effectiveness of quality-guided gallery selection. Template-fusion strategies and latent fingerprint recognition technologies are further examined to identify the best possible matching rates achievable by the top-performing method. Results show that tuned parameter sets outperform default configurations for the majority of methods and age groups, indicating that targeted recalibration of existing frameworks improves gallery utility and contributes to more reliable infant fingerprint recognition.