Biometric recognition of newborns and young children for vaccinations and health care: a non-randomized prospective clinical trial

Abstract

Although universal biometrics have been broadly called for, and there are many validated technologies to recognize adults, these technologies have been ineffective in newborns and young children. The present work describes the development and clinical testing of a fingerprint capture system for longitudinal biometric recognition of newborns and young children to support vaccination and clinical follow-up. The reader consists of a high-resolution monochromatic imaging system with an ergonomic industrial design to comfortably support and align infant fingers for imaging without a platen. This imaging approach without a platen, also called free-space imaging, reduces fingerprint distortion and ensures a more consistent finger placement. This system was tested in a newborn ward and immunization clinic at an urban hospital in Baja, California, Mexico, from 2017 to 2019. Nearly five hundred children were enrolled and followed for up to 24 months. With a protocol of imaging all ten fingers, the failure to enroll (FTE) rate was < 1% when acquiring at least two fingers for all ages and < 2% when enrolling at least four fingers. The verification (1:1) true accept rate (TAR) was 77% for newborns enrolled at ≤ 3 days of age and 96% for those enrolled at ≥ 4 days of age, both at a time gap of 15-30 days after enrollment at a false accept rate (FAR) of 0.1%. Using the top-ranked match score, the identification rate (1:many) was 86% for the ≤ 3 days enrollment age and 97% for age ≥ 4 days for a single finger at 15-30 days after enrollment. The enrollment protocol and the frequency of updating will increase for infants compared to adults. However, these data suggest that a high-resolution, free space imaging technique may fill the final gap for universal biometrics across all populations called for by the United Nations Sustainable Development Goal 16.9.

Figure 1

The biometric device and example imagery used in this study. (A) the device in use, (B) time-course images of a single subject’s finger, (C) binarized prints finger images, and (d) the internal components and design of the “Panda” device.

Figure 2

Effect of enrollment age and number of fingers used on the verification performance for (Left) 0–3 day age enrollment and (Right) > 3 day age enrollment with a verification time gap of 15–30 days.

Figure 3

The effect of age at enrollment and time gaps between enrollment and verification up to 180 days, for (Left) enrollment age group 0–3 days and (Right) for the enrollment age group of 4 days and older.

Figure 4

CMC analysis as a function of enrollment age, time gap, and the number of fingers.