An on-production high-resolution longitudinal neonatal fingerprint database in Brazil

TL;DR

Neonatal identification is hampered by rapid finger growth and fingerprint aging effects. The authors design a high-resolution, longitudinal neonatal fingerprint dataset with ten-finger captures and multiple follow-ups, and propose deep learning models to emulate growth in minutiae beyond simple scaling. They report enrollment of over 450 infants and 829 follow-up sessions, yielding approximately 9.9k video files totaling ~776 GB, establishing one of the most comprehensive longitudinal neonatal biometric datasets to date. The work aims to enable robust, growth-aware infant biometrics that can enhance healthcare delivery, vaccination tracking, nutrition monitoring, and child protection in low-resource settings.

Abstract

The neonatal period is critical for survival, requiring accurate and early identification to enable timely interventions such as vaccinations, HIV treatment, and nutrition programs. Biometric solutions offer potential for child protection by helping to prevent baby swaps, locate missing children, and support national identity systems. However, developing effective biometric identification systems for newborns remains a major challenge due to the physiological variability caused by finger growth, weight changes, and skin texture alterations during early development. Current literature has attempted to address these issues by applying scaling factors to emulate growth-induced distortions in minutiae maps, but such approaches fail to capture the complex and non-linear growth patterns of infants. A key barrier to progress in this domain is the lack of comprehensive, longitudinal biometric datasets capturing the evolution of neonatal fingerprints over time. This study addresses this gap by focusing on designing and developing a high-quality biometric database of neonatal fingerprints, acquired at multiple early life stages. The dataset is intended to support the training and evaluation of machine learning models aimed at emulating the effects of growth on biometric features. We hypothesize that such a dataset will enable the development of more robust and accurate Deep Learning-based models, capable of predicting changes in the minutiae map with higher fidelity than conventional scaling-based methods. Ultimately, this effort lays the groundwork for more reliable biometric identification systems tailored to the unique developmental trajectory of newborns.

Figures (2)

• Figure 1: Collection process
• Figure 2: Example of prints of one child across time, with the respective segmentation and minutiae map