Iris Recognition for Infants
Journal:
arXiv
Published Date:
Jan 2, 2025
Abstract
Non-invasive, efficient, physical token-less, accurate and stable
identification methods for newborns may prevent baby swapping at birth, limit
baby abductions and improve post-natal health monitoring across geographies,
within the context of both the formal (i.e., hospitals) and informal (i.e.,
humanitarian and fragile settings) health sectors. This paper explores the
feasibility of application iris recognition to build biometric identifiers for
4-6 week old infants. We (a) collected near infrared (NIR) iris images from 17
infants using a specially-designed NIR iris sensor; (b) evaluated six iris
recognition methods to assess readiness of the state-of-the-art iris
recognition to be applied to newborns and infants; (c) proposed a new
segmentation model that correctly detects iris texture within infants iris
images, and coupled it with several iris texture encoding approaches to offer,
to the first of our knowledge, a fully-operational infant iris recognition
system; and, (d) trained a StyleGAN-based model to synthesize iris images
mimicking samples acquired from infants to deliver to the research community
privacy-safe infant iris images. The proposed system, incorporating the
specially-designed iris sensor and segmenter, and applied to the collected
infant iris samples, achieved Equal Error Rate (EER) of 3\% and Area Under ROC
Curve (AUC) of 99\%, compared to EER$\geq$20\% and AUC$\leq$88\% obtained for
state of the art adult iris recognition systems. This suggests that it may be
feasible to design methods that succesfully extract biometric features from
infant irises.
