KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis

KA-GCN: Kernel-Attentive Graph Convolutional Network for 3D face analysis

Graph Structure Learning (GSL) methods address the limitations of real-world graphs by refining their structure and representation. This allows Graph Neural Networks (GNNs) to be applied to broader unstructured domains such as 3D face analysis. GSL can be considered as the dynamic learning of connec...

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Bibliographic Details
Main Authors: Francesco Agnelli, Giuseppe Facchi, Giuliano Grossi, Raffaella Lanzarotti
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Array
Subjects:
Graph Structure Learning (GSL)
3D face analysis
FaceScape dataset
Headspace dataset
Florence dataset
Sexual dimorphism recognition
Online Access:http://www.sciencedirect.com/science/article/pii/S2590005625000190
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http://www.sciencedirect.com/science/article/pii/S2590005625000190

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