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Article ; Online: Unveiling facial kinship: The BioKinVis dataset for facial kinship verification and genetic association studies.

Yu, Jian / Jin, Xiaozhe / Du, Weijie / Bai, Yantong / Zhou, Xin / Gao, Mengli / Li, Shuwen / Qin, Jiarui / Chen, Xuanlong / Liu, Yuhao / Yu, Jianing / Chen, Chen / Xie, Qiheng / Xie, Sumei / Kong, Xiaochao / Zhan, Wenxuan / Yu, Yanfang / Li, Kai / Ji, Qiang /
Chen, Feng / Chen, Peng

Electrophoresis

2023  

Abstract: 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 ... ...

Abstract 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/.
Language English
Publishing date 2023-12-31
Publishing country Germany
Document type Journal Article
ZDB-ID 619001-7
ISSN 1522-2683 ; 0173-0835
ISSN (online) 1522-2683
ISSN 0173-0835
DOI 10.1002/elps.202300169
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