用对比学习验证指纹与虹膜生物特征的相关性
Bi-Encoder Contrastive Learning for Fingerprint and Iris Biometrics
- 采用双编码器架构和对比损失，训练模型匹配同一人的指纹与虹膜
- 虹膜匹配达91 ROC AUC，证明左右虹膜存在显著相关性
- 首次用视觉变换器做此类任务，适合生物识别研究者参考
传统观点认为个体的生物特征在统计上相互独立。我们通过在三个验证任务上训练双编码器网络进行检验：指纹-指纹匹配、虹膜-虹膜匹配及跨模态指纹-虹膜匹配，使用274名受试者约10万张指纹和7,000张虹膜图像。在双编码器架构中分别采用ResNet-50与视觉变换器（Vision Transformer）作为主干网络，最小化同一个人样本间的对比损失。虹膜-虹膜匹配的ResNet模型达到91的ROC AUC，明确表明个体左右虹膜之间存在相关性。指纹模型复现了先前研究中提出的同类内正向关联。这是首个尝试将视觉变换器应用于该类匹配任务的工作。跨模态匹配性能仅略高于随机水平，表明需更多数据和更复杂流程才能取得有说服力的结果。这些发现挑战了生物特征独立性的传统假设，未来计划扩展至其他生物特征。代码已公开：https://github.com/MatthewSo/bio_fingerprints_iris。
原文摘要 · Abstract (English)
There has been a historic assumption that the biometrics of an individual are statistically uncorrelated. We test this assumption by training Bi-Encoder networks on three verification tasks, including fingerprint-to-fingerprint matching, iris-to-iris matching, and cross-modal fingerprint-to-iris matching using 274 subjects with $\sim$100k fingerprints and 7k iris images. We trained ResNet-50 and Vision Transformer backbones in Bi-Encoder architectures such that the contrastive loss between images sampled from the same individual is minimized. The iris ResNet architecture reaches 91 ROC AUC score for iris-to-iris matching, providing clear evidence that the left and right irises of an individual are correlated. Fingerprint models reproduce the positive intra-subject suggested by prior work in this space. This is the first work attempting to use Vision Transformers for this matching. Cross-modal matching rises only slightly above chance, which suggests that more data and a more sophisticated pipeline is needed to obtain compelling results. These findings continue challenge independence assumptions of biometrics and we plan to extend this work to other biometrics in the future. Code available: https://github.com/MatthewSo/bio_fingerprints_iris.
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