Kinship Verification from Text: Towards Discovering Subtitle Textual Features Shared by Family Members using Large Language Models

The objective of kinship verification is to assess whether two individuals are biologically related or not. Previous research has primarily focused on determining kinship from facial patterns and movements, voice, or human gait. In this paper, we explore for the first time in the literature the problem of kinship verification from text. This is a very timely topic given the emergence of generative artificial intelligence and large language models. Our main hypothesis is that two family members inherit and share subtitle textual features that can be seen in the way of writing. The subtle features can be related to genes, culture, experience etc. To address this problem, we propose a Siamese-based BERT transformer incorporating two novel modules, namely Attention and Fusion. The experiments show promising results on the role textual information in kinship verification.