Facial image–based kinship verification represents a burgeoning frontier within the realms of computer vision and biomedicine. Recent genome-wide association studies have underscored the heritability of human facial morphology, revealing its predictability based on genetic information. These revelations form a robust foundation for advancing facial image–based kinship verification. Despite strides in computer vision, there remains a discernible gap between the biomedical and computer vision domains. Notably, the absence of family photo datasets established through biological paternity testing methods poses a significant challenge. This study addresses this gap by introducing the biological kinship visualization dataset, encompassing 5773 individuals from 2412 families with biologically confirmed kinship. Our analysis delves into the distribution and influencing factors of facial similarity among parent–child pairs, probing the potential association between forensic short tandem repeat polymorphisms and facial similarity. Additionally, we have developed a machine learning model for facial image–based kinship verification, achieving an accuracy of 0.80 in the dataset. To facilitate further exploration, we have established an online tool and database, accessible at http://120.55.161.230:88/.

中文翻译：

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揭开面部亲缘关系：用于面部亲缘关系验证和遗传关联研究的 BioKinVis 数据集

基于面部图像的亲属关系验证代表了计算机视觉和生物医学领域的一个新兴前沿。最近的全基因组关联研究强调了人类面部形态的遗传性，揭示了其基于遗传信息的可预测性。这些发现为推进基于面部图像的亲属关系验证奠定了坚实的基础。尽管计算机视觉取得了长足的进步，但生物医学和计算机视觉领域之间仍然存在明显的差距。值得注意的是，缺乏通过生物亲子鉴定方法建立的家庭照片数据集构成了重大挑战。本研究通过引入生物亲属关系可视化数据集来解决这一差距，该数据集包含来自 2412 个具有生物学确认的亲属关系的家庭的 5773 名个体。我们的分析深入研究了亲子对面部相似性的分布和影响因素，探讨了法医短串联重复多态性与面部相似性之间的潜在关联。此外，我们还开发了一种基于面部图像的亲属关系验证的机器学习模型，在数据集中实现了 0.80 的准确度。为了便于进一步探索，我们建立了在线工具和数据库，可通过http://120.55.161.230:88/访问。