  Facial Kinship Verification is the task of determining the degree of familial
relationship between two facial images. It has recently gained a lot of
interest in various applications spanning forensic science, social media, and
demographic studies. In the past decade, deep learning-based approaches have
emerged as a promising solution to this problem, achieving state-of-the-art
performance. In this paper, we propose a novel method for solving kinship
verification by using supervised contrastive learning, which trains the model
to maximize the similarity between related individuals and minimize it between
unrelated individuals. Our experiments show state-of-the-art results and
achieve 81.1% accuracy in the Families in the Wild (FIW) dataset.
