Siamese Neural Networks and Transfer Learning for Kinship Verification from Dermal Palm Images

Section: Research Paper
Issue
Vol. 30 No. 2 (2025): Volume 30 Issue 2
Published
Sep 1, 2025
Pages
90-103

Abstract

Kinship verification is a crucial research area due to its diverse applications, including paternity tests, family reunions, and criminal investigations. While DNA analysis has been the predominant method, artificial intelligence techniques are still being explored and tested. Facial kinship verification, which involves comparing features between two facial images, has garnered significant research interest. This paper introduces a new approach to kinship verification using hand-palm images. The EfficientNetB0 model was utilized for deep feature extraction through transfer learning. A Siamese neural network architecture was employed to assess similarity. Various experimental scenarios were conducted concerning network architecture, training parameters, and fine-tuning. The Mosul Kinship Hand (MKH) dataset was used to create the palm dermal image dataset, consisting of 7,332 pairs equally divided into related and unrelated categories. The results were promising, achieving approximately 99% validation accuracy, and 77.02 ms average inference time per image pair using a post-training Principal Component Analysis (PCA) technique.

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Author

Mazin H. Aziz

Computer Engineering Department, College of Engineering, University of Mosul, Mosul, Iraq

ORCID

Identifiers

https://doi.org/10.33899/arej.2025.159532.1412
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[1]
M. H. Aziz, “Siamese Neural Networks and Transfer Learning for Kinship Verification from Dermal Palm Images”, AREJ, vol. 30, no. 2, pp. 90–103, Sep. 2025.
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