• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2024, Volume: 17, Issue: 4, Pages: 352-358

Original Article

Siamese Neural Networks for Kinship Prediction: A Deep Convolutional Neural Network Approach

Received Date:26 November 2023, Accepted Date:28 December 2023, Published Date:20 January 2024


Objective: This study worked out kinship verification, which is a laborious problem in scientific discipline and pattern discovery. It has many applications, such as: finding missing children, identify family and non family member. The aim is to kinship prediction using similarity computation to identify kin and non-kin based on image dataset. Method: To measure the similarity score of the proposed method on primary 96-family dataset, considers 410 images and 77,887 different pairs. The data was split into 80% for training and 20% for testing. This study proposed Siamese Deep Convolutional Neural Network model with deep algorithm viz., ResNet and VGGNet with Adam optimizer to verify the kinship for the four kinship relations of father-son, father-daughter, mother-son and mother-daughter. Findings: It is observed that, the proposed model gives better perform and with 72.73% average similarity score. Novelty: Experimental results on the primary kinship datasets showed the superior performance of the proposed methods over state-of-the-art kinship verification methods and human ability in our kinship verification task. In the future, this model can be applicable for kinship verification in society.

Keywords: Deep learning, Image­based kinship verification, Convolutional Neural Network, Siamese Neural Network, Adam, ResNet, VGGNet


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© 2024 Navghare et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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