通过迭代纠错生成婴儿指纹多样性数据，提升小样本匹配系统性能。
Iterative Framework For Data Augmentation Of Segmented Fingerprints
- 用卷积网络错误诱导生成指纹变体，实现可控数据增强
- 增广后指纹纹线点数波动显著，仍保持与原图视觉相似
- 方法可调参数，适合婴儿生物识别等小样本场景研究
婴儿生物特征因生理差异与数据稀缺，给匹配系统研发带来挑战。本文提出一种新型数据增强方法，通过在训练提取指纹纹线与谷线的卷积神经网络中引入误差，迭代生成分割指纹的多样化变体。实验证明该方法能有效扩展指纹变异范围，增广样本在纹线点数量上呈现显著波动，同时保持与原始图像的视觉相似性。研究还展示了方法的可定制性，可灵活调节分割结果的改变程度。未来工作将基于该框架构建的增强数据集，训练分割与匹配神经网络。
原文摘要 · Abstract (English)
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.
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