Kinship Representation Learning with Face Componential Relation

• URL: http://arxiv.org/abs/2304.04546v5
• Date: Sat, 30 Sep 2023 01:10:44 GMT
• Title: Kinship Representation Learning with Face Componential Relation
• Authors: Weng-Tai Su, Min-Hung Chen, Chien-Yi Wang, Shang-Hong Lai, Trista Pei-Chun Chen
• Abstract summary: Kinship recognition aims to determine whether the subjects in two facial images are kin or non-kin. Most previous methods focus on designs without considering the spatial correlation between face images. We propose the Face Componential Relation Network, which learns the relationship between face components among images with a cross-attention mechanism. The proposed FaCoRNet outperforms previous state-of-the-art methods by large margins for the largest public kinship recognition FIW benchmark.
• Score: 19.175823975322356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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