Family Membership Detection from Palm-Skin Texture Using Transfer Learning: A New Kinship Approach

The detection of a person's membership in a family is crucial in many cases, and the need for it becomes urgent during and after wars and natural disasters. While DNA testing and facial images are common methods for kinship detection, they have limitations in certain scenarios. This paper presents an innovative approach to intrafamily kinship classification using palm skin texture images, a modality not previously explored for this purpose. Unlike conventional palm biometric verification aimed at individual identification, this research investigates inherited skin texture patterns to establish familial relationships. A hand palm dataset was created from the MKH (Mosul Kinship Hand) images dataset. It contains 332 segmented and annotated skin images from 84 individuals across 15 families, with multiple samples per person. Two deep convolutional neural network pre-trained models (VGG16 and EfficientNetB0) were used independently for feature extraction via transfer learning. Based on these features, neural network classifiers were designed to detect the membership of an individual to one of the given families. The results were evaluated using the appropriate metrics and gave a test accuracy of 96% by using EfficientNetB0 and 79% from VGG16. The findings of this study suggest that hand palm images may offer a promising and practical preliminary approach to kinship detection. The dataset utilized in this study is accessible upon request from the author to support reproducibility and encourage further research.