  Kinship recognition aims to determine whether the subjects in two facial
images are kin or non-kin, which is an emerging and challenging problem.
However, most previous methods focus on heuristic designs without considering
the spatial correlation between face images. In this paper, we aim to learn
discriminative kinship representations embedded with the relation information
between face components (e.g., eyes, nose, etc.). To achieve this goal, we
propose the Face Componential Relation Network, which learns the relationship
between face components among images with a cross-attention mechanism, which
automatically learns the important facial regions for kinship recognition.
Moreover, we propose Face Componential Relation Network (FaCoRNet), which
adapts the loss function by the guidance from cross-attention to learn more
discriminative feature representations. The proposed FaCoRNet outperforms
previous state-of-the-art methods by large margins for the largest public
kinship recognition FIW benchmark.
