Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement

2026-05-24

AI Summary

This study addresses the challenge of poor fingerprint image quality in infants, which stems from their small finger size and fine ridge structures, leading to significantly lower recognition accuracy compared to adults. To overcome this, the authors propose a Recursive Ridge Connectivity Classification (R3C) framework that iteratively fuses binary segmentation outputs with the original image and feeds the result back into a classifier. Notably, R3C operates without requiring additional training data or modifications to the underlying enhancement algorithms, enabling automatic extension and reconnection of broken ridges. The method is highly versatile and consistently improves fingerprint recognition performance for both children and newborns: across three datasets combined with four enhancement techniques, it achieves up to a 4% increase in true acceptance rate (TAR) for children and over 40% for newborns, while also substantially enhancing the visual quality of ridge segmentation.

Abstract

Image enhancement plays a crucial role in infant fingerprint matching, as child-specific characteristics such as smaller finger dimensions and thinner ridge structures often degrade image quality during acquisition. To address these limitations, enrollment typically depends on specialized highresolution scanners, which most existing enhancement methods are not designed to support. Consequently, identification rates for children remain significantly lower than those achieved with adult fingerprints. This study introduces Recursive Class Connectivity Classification (R3C), a novel framework that iteratively refines binary segmentation outputs from existing enhancement methods by extending ridge structures. R3C does not require modifications to the underlying classifier and operates without training data, which is not currently available for infant fingerprints. Instead, the method improves segmentation by repeatedly feeding the classified image back into the classification process, while combining each intermediate segmentation with the original input image. Experiments conducted on three fingerprint datasets using four different enhancement classifiers show that R3C can increase the True Acceptance Rate (TAR) by up to 4% for children and over 40% for newborns, compared to using the enhancement methods alone. A qualitative analysis further demonstrates that R3C reconnects fragmented ridge patterns, improving the visual quality of segmentation. Because it functions independently of the enhancement method used, R3C provides a flexible and broadly applicable solution for improving binary segmentation.

Problem

• infant fingerprint
• image enhancement
• binary segmentation
• fingerprint recognition
• ridge connectivity

Innovation

• Recursive Class Connectivity Classification
• binary image segmentation
• infant fingerprint enhancement
• ridge connectivity
• training-free refinement