Child PalmID: Contactless Palmprint Recognition

TL;DR

This paper evaluates a contactless palmprint recognition system for children aged 1-5, achieving high accuracy and demonstrating its potential for biometric identification in humanitarian aid contexts.

Contribution

It establishes baseline accuracy metrics for a commercial palmprint recognition system applied to young children, a novel application area.

Findings

Achieved 90.85% authentication accuracy at FAR of 0.01%

Achieved 99.0% rank-1 identification accuracy in closed-set tests

Demonstrated system's suitability for open-set identification with FPIR=0.01 at FNIR=0.3

Abstract

Developing and least developed countries face the dire challenge of ensuring that each child in their country receives required doses of vaccination, adequate nutrition and proper medication. International agencies such as UNICEF, WHO and WFP, among other organizations, strive to find innovative solutions to determine which child has received the benefits and which have not. Biometric recognition systems have been sought out to help solve this problem. To that end, this report establishes a baseline accuracy of a commercial contactless palmprint recognition system that may be deployed for recognizing children in the age group of one to five years old. On a database of contactless palmprint images of one thousand unique palms from 500 children, we establish SOTA authentication accuracy of 90.85% @ FAR of 0.01%, rank-1 identification accuracy of 99.0% (closed set), and FPIR=0.01 @ FNIR=0.3 for open-set identification using PalmMobile SDK from Armatura.