Kinship Verification in Childhood Images Using Vision Transformer

Facial Kinship Verification involves determining whether two face images belong to relatives, a task that is particularly challenging due to subtle differences in facial features and large intra-class variations. In recent years, deep learning models have shown great promise in addressing this probl...

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Vydáno v:Procedia computer science Ročník 258; s. 3105 - 3114
Hlavní autoři: Oruganti, Madhu, Meenpal, Toshanlal, Majumdar, Saikat, Tekchandani, Hitesh
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 2025
Témata:
Accuracy
Binary Classification Problem
Childhood Images
F1 Score
Facial Kinship Verification
Vision Transformers
Accuracy
F1 Score
Vision Transformers
Childhood Images
Facial Kinship Verification
Binary Classification Problem
ISSN:1877-0509, 1877-0509
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Shrnutí:Facial Kinship Verification involves determining whether two face images belong to relatives, a task that is particularly challenging due to subtle differences in facial features and large intra-class variations. In recent years, deep learning models have shown great promise in addressing this problem. In this work, we propose a Vision Transformer (ViT) model for facial Kinship Verification, leveraging the proven effectiveness of Transformer architectures in Natural Language Processing. The Vision Transformer is trained end-to-end on two benchmark datasets: the large-scale Families in the Wild (FIW) dataset, consisting of thousands of face images with corresponding kinship labels, and the smaller KinFaceW-II dataset. Our model employs multiple attention mechanisms to capture complex relationships between facial features and produce a final kinship prediction. Experimental results demonstrate that our approach outperforms state-of-the-art methods, achieving an average accuracy of 92% on the FIW dataset and an F1 score of 0.85. The Euclidean distance metric further enhances the classification of kin and non-kin pairs. These findings confirm the effectiveness of Vision Transformer models for facial Kinship Verification and underscore their potential for future research in this domain.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2025.04.568