Iterative Framework For Data Augmentation Of Segmented Fingerprints

2026-05-29

AI Summary

This study addresses the challenge of insufficient robustness in infant fingerprint recognition, which stems from weak physiological features and scarce training data. To overcome this limitation, the authors propose a controllable data augmentation method based on iterative error injection. Within a convolutional neural network–driven pipeline for fingerprint segmentation and ridge extraction, the approach introduces tunable structural perturbations that preserve visual consistency while substantially enriching the diversity of minutiae features. Experimental results demonstrate that the augmented samples retain the original fingerprint morphology yet exhibit significantly varied minutiae configurations, thereby enhancing the robustness of downstream segmentation and matching models. This work offers a novel and effective strategy for improving biometric recognition performance in low-data regimes, particularly for infant fingerprints.

Abstract

Infant biometrics presents unique challenges due to the physiological differences between infants and adults, compounded by the scarcity of available data for research that limits the development of robust matching systems. This paper proposes a novel data augmentation method that uses iterative techniques to generate diverse variants of segmented fingerprints by inducing errors in a convolutional neural network trained to extract fingerprint ridges and valleys. Experiments on real infant fingerprints demonstrate the method's effectiveness in expanding fingerprint variability, with augmentations exhibiting significant fluctuations in minutiae counts while still retaining visual similarity to the originals. The study also highlights the method's customizable nature for applying varying levels of changes to fingerprint segmentations. Future research includes training segmentation and matching neural networks using datasets augmented by the proposed framework.

Problem

• infant biometrics
• data scarcity
• fingerprint segmentation
• biometric matching
• data augmentation

Innovation

• data augmentation
• iterative framework
• infant biometrics
• fingerprint segmentation
• convolutional neural network