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Kinship Verification by Analysing Facial Images to Ensure Human Security
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Author(s): Arkadyuti Mahato (Dr. B.C. Roy Engineering College, India), Sourav Das (Dr. B.C. Roy Engineering College, India), Sumana Kundu (Dr. B.C. Roy Engineering College, India)and Anandaprova Majumder (Dr. B.C. Roy Engineering College, India)
Copyright: 2025
Pages: 18
Source title:
Human Impact on Security and Privacy: Network and Human Security, Social Media, and Devices
Source Author(s)/Editor(s): Rajeev Kumar (Moradabad Institute of Technology, India), Saurabh Srivastava (Moradabad Institute of Technology, India)and Ahmed A. Elngar (Beni-Suef University, Egypt)
DOI: 10.4018/979-8-3693-9235-5.ch011
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Abstract
Kinship verification involves determining the relatedness between individuals by analysing their genetic or phenotypic characteristics or traits. The realm of kinship research has garnered substantial attention particularly in computer vision, with broad applications spanning forensic analysis, genealogy exploration, immigration law and more. This chapter introduces a five-stage methodology for kinship verification through facial images, employing Convolutional Neural Networks (CNNs). The process involves dataset acquisition, preprocessing with FaceNet and Res-Net-50, feature extraction, and CNN model development utilizing Inception Resnet V1 and ResNet-50. The proposed model attains a remarkable accuracy exceeding 85% on a substantial dataset, showcasing its efficacy in identifying kinship relations. Thus, the proposed work contributes to reshaping the methodology for kin verification, enhancing human surveillance through the development of facial image analysis techniques with immense potential for practical applications.
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