Enhancing 3D Face Analysis Using Graph Convolutional Networks with Kernel-Attentive Filters

Graph Structure Learning (GSL) techniques improve real-world graph representations, enabling Graph Neural Networks (GNNs) to be applied to unstructured domains like 3D face analysis. GSL dynamically learns connection weights within message-passing layers of a GNN, particularly in Graph Convolutional Networks (GCNs). This becomes crucial when working with small sample datasets, where methods like Transformers, which require large data for training, are less effective. In this paper, we introduce a kernel-attentive Graph Convolutional Network (KA-GCN) designed to integrate a positional bias through attention mechanisms into small-scale 3D face analysis tasks. This approach combines kerneland attention-based mechanisms to adjust different distance measures and learn the adjacency matrix. Extensive experiments on the FaceScape public dataset and a private dataset featuring facial dysmorphisms show that our method outperforms state-of-the-art models in both effectiveness and robustness. The code is available at https://github.com/phuselab/KA-CONV.