A performance evaluation of convolution neural networks for kinship discernment: An application in digital forensics

Article type: Research Article

Authors: A, Harisha^* | Prasad, B. Krishna | Rajeev, Keerthana | Maithri, | Nishchal,

Affiliations: Department of Computer Science and Engineering, Sahyadri College of Engineering and Management, Mangalore, India

Correspondence: [*] Corresponding author: Harisha A, Department of Computer Science and Engineering, Sahyadri College of Engineering and Management Mangaluru, Adyar 575005, India. E-mail: [email protected].

Abstract: Kinship Verification from facial images is known to have attracted major attention since time immemorial. Identifying the underlying patterns that exist between images and analysing the relationship hidden between them have enabled the multitudes of applications to utilize kinship relationships. This work serves as a study on the amount of influence that hereditary features can exert on the families tied together by lineage in identifying the relationship prevailing between them and whether it holds true or not. The approach employed involves detecting the relationship existing between the provided facial images using Siamese Network, which comprises two identical convolutional neural networks that share common weight values. A difference vector is computed from this Siamese CNN, which is then fed into a network of fully connected linear layers. This extended layer will determine whether the two individuals in the input images are related to each other or not.

Keywords: Convolution neural network, siamese network, deep learning, kinship detection, artificial intelligence

DOI: 10.3233/IDT-210132

Journal: Intelligent Decision Technologies, vol. 16, no. 2, pp. 379-386, 2022