PDF(Pubmed)
Abstract：
Fingerprint biometrics are integral to digital authentication and forensic science. However, they are based on the unproven assumption that no two fingerprints, even from different fingers of the same person, are alike. This renders them useless in scenarios where the presented fingerprints are from different fingers than those on record. Contrary to this prevailing assumption, we show above 99.99% confidence that fingerprints from different fingers of the same person share very strong similarities. Using deep twin neural networks to extract fingerprint representation vectors, we find that these similarities hold across all pairs of fingers within the same person, even when controlling for spurious factors like sensor modality. We also find evidence that ridge orientation, especially near the fingerprint center, explains a substantial part of this similarity, whereas minutiae used in traditional methods are almost nonpredictive. Our experiments suggest that, in some situations, this relationship can increase forensic investigation efficiency by almost two orders of magnitude.
摘要：
指纹生物识别技术是数字认证和法医学不可或缺的一部分。然而,它们基于未经证实的假设，即没有两个指纹，甚至来自同一个人的不同手指，都是一样的。这使得它们在所呈现的指纹来自与记录上的手指不同的手指的场景中无用。与这个普遍的假设相反，我们显示出99.99%以上的信心，来自同一个人不同手指的指纹具有非常强的相似性。利用深度孪生神经网络提取指纹表示向量,我们发现这些相似性在同一个人的所有手指上都存在，即使在控制传感器模态等虚假因素时。我们还发现了山脊取向的证据，尤其是在指纹中心附近，解释了这种相似性的很大一部分，而传统方法中使用的细节几乎是不可预测的。我们的实验表明,在某些情况下,这种关系可以使法医调查效率提高近两个数量级。
