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.

翻译：亲属关系识别旨在确定两张面部图像中的主体是否存在亲缘关系，这是一个新兴且具有挑战性的问题。然而，以往大多数方法侧重于启发式设计，未能考虑面部图像之间的空间关联性。本文旨在学习嵌入面部组件（如眼睛、鼻子等）间关系信息的判别性亲属关系表示。为实现此目标，我们提出了面部组件关系网络（Face Componential Relation Network, FaCoRNet），该网络通过交叉注意力机制学习图像间面部组件的关系，从而自动识别对亲属关系识别重要的面部区域。此外，我们通过交叉注意力的指导对损失函数进行自适应调整，以学习更具判别性的特征表示。所提出的FaCoRNet在最大的公开亲属关系识别基准FIW上，以显著优势超越了现有最先进方法。