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 \MainMethodAbbr~outperforms previous state-of-the-art methods by large margins for the largest public kinship recognition FIW benchmark. The code will be publicly released upon acceptance.

翻译：亲属关系识别旨在判断两张人脸图像中的个体是否存在亲属关系，这是一个新兴且具有挑战性的问题。然而，以往多数方法侧重于启发式设计，未考虑人脸图像之间的空间相关性。本文旨在学习嵌入了人脸组件（如眼睛、鼻子等）之间关系信息的判别性亲属关系表示。为此，我们提出人脸组件关系网络，该网络通过交叉注意力机制学习图像间人脸组件的关系，自动识别对亲属关系识别重要的人脸区域。此外，我们进一步提出FaCoRNet，该网络利用交叉注意力指导损失函数优化，以学习更具判别性的特征表示。所提出的方法在最大的公开亲属关系识别基准FIW上，以大幅优势超越现有最优方法。代码将在论文接收后公开发布。