一种用于检测两幅虹膜图像是否来自同卵双胞胎的孪生网络
A Siamese Network to Detect If Two Iris Images Are Monozygotic
AI总结：
本研究提出首个基于 Siamese network 和对比学习的自动化分类器，用于判定一对虹膜图像是否来自同卵双胞胎，在使用完整虹膜图像时其准确率超越了人类分类水平。
摘要：
本研究提出了首个旨在确定一对虹膜图像是否来自同卵双胞胎个体的自动化分类器，解决了生物特征识别中一个此前未被处理的问题。在 Daugman 式虹膜识别中，同一个人的左右虹膜纹理传统上被认为与两个无血缘关系的人的虹膜一样不同。然而，先前的研究表明，人类能够以约 80% 的准确率检测出两幅虹膜图像是来自同一个人的不同眼睛，还是来自同卵双胞胎的眼睛。在本研究中，我们采用 Siamese network 架构和对比学习，将一对虹膜图像分类为来自同卵或非同卵虹膜。例如，这可以作为一种快速、无创的测试方法，用于确定双胞胎是同卵还是非同卵。我们构建了一个数据集，包含合成同卵对（同一个人的不同虹膜图像）和自然同卵对（同卵双胞胎个体的不同虹膜图像），以及来自无血缘关系个体的非同卵对，从而确保对模型能力进行全面评估。为了更深入地理解学习到的表示，我们训练并分析了模型的三个变体，分别使用：(1) 原始输入图像，(2) 仅虹膜图像（遮蔽虹膜区域以外的所有部分），以及 (3) 非仅虹膜图像（遮蔽虹膜区域）。这一比较表明，虹膜纹理和周围的眼睛结构都包含有助于模型将图像对分类为同卵或非同卵的信息。我们的方法在使用完整虹膜图像时达到了准确率水平，超过了此前报道的人类对同卵虹膜对进行分类的水平。
英文摘要：
This study presents the first automated classifier designed to determine whether a pair of iris images originates from monozygotic individuals, addressing a previously untackled problem in biometric recognition. In Daugman-style iris recognition, the textures of the left and right irises of the same person are traditionally considered as being as different as the irises of two unrelated persons. However, previous research indicates that humans can detect that two iris images are from different eyes of the same person, or eyes of monozygotic twins, with an accuracy of about 80%. In this work, we employ a Siamese network architecture and contrastive learning to categorize a pair of iris images as coming from monozygotic or non-monozygotic irises. This could potentially be applied, for example, as a fast, noninvasive test to determine if twins are monozygotic or non-monozygotic. We construct a dataset comprising both synthetic monozygotic pairs (images of different irises of the same individual) and natural monozygotic pairs (images of different images from persons who are identical twins), in addition to non-monozygotic pairs from unrelated individuals, ensuring a comprehensive evaluation of the model's capabilities. To gain deeper insights into the learned representations, we train and analyze three variants of the model using (1) the original input images, (2) iris-only images (masking everything but the iris region), and (3) non-iris-only images (masking the iris region). This comparison reveals that both iris texture and surrounding ocular structure contain information useful for the model to classify the image pairs as monozygotic or non-monozygotic. Our approach achieves accuracy levels using the full iris image that exceed those previously reported for human classification of monozygotic iris pairs.
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