Iterative Framework For Data Augmentation Of Segmented Fingerprints
This work tackles the problem of limited infant biometric data, which is crucial for developing robust matching systems for infants.
This paper addresses the scarcity of infant biometric data by proposing a novel iterative data augmentation method. It generates diverse variants of segmented fingerprints by inducing errors in a CNN, demonstrating significant fluctuations in minutiae counts while maintaining visual similarity to original infant fingerprints.
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.