Department of Computer Science, Columbia University, New York, NY 10027, USA.
Department of Computer Science, Tufts University, Medford, MA 02155, USA.
Sci Adv. 2024 Jan 12;10(2):eadi0329. doi: 10.1126/sciadv.adi0329.
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.
指纹生物识别技术是数字认证和法医学的重要组成部分。然而，它们基于一个未经证实的假设，即没有两个指纹（即使来自同一个人的不同手指）是相同的。这使得它们在呈现的指纹与记录中的指纹来自不同手指的情况下毫无用处。与这一普遍假设相反，我们以超过 99.99%的置信度证明，来自同一个人不同手指的指纹具有非常强的相似性。我们使用深度孪生神经网络提取指纹表示向量，发现即使在控制传感器模态等虚假因素的情况下，这些相似性也存在于同一个人所有的手指对之间。我们还发现证据表明，脊线方向（尤其是指纹中心附近）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点几乎是不可预测的。我们的实验表明，在某些情况下，这种关系可以将法医调查效率提高近两个数量级。
