A graph convolutional network face recognition algorithm based on clustering

Wenhao Jiang; Ying Long

doi:10.1117/12.3045924

16 January 2025 A graph convolutional network face recognition algorithm based on clustering

Wenhao Jiang, Ying Long

Author Affiliations +

Proceedings Volume 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024); 1344751 (2025) https://doi.org/10.1117/12.3045924
Event: International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 2024, Wuhan, China

Abstract

Graph Convolutional Networks (GCN) have emerged as effective models in graph data analysis and Deep Learning tasks, achieving significant advancements in recent years. In this paper, we propose GCN based on clustering algorithm for face recognition. By transforming the clustering problem into two sub-problems, two graph convolutional networks are employed, one graph convolutional network estimates the confidence of vertices in the graph, and another graph convolutional network estimates the connectivity of edges in the graph. This approach effectively captures both local and global information among face images. Experimental results demonstrate that the clustering-based GCN model achieves state-of-the-art performance on various public face datasets, highlighting the effectiveness and potential of clusteringbased GCN in face recognition tasks. These findings provide valuable insights for further improving and applying GCN models and serve as a beneficial reference in this field.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenhao Jiang and Ying Long "A graph convolutional network face recognition algorithm based on clustering", Proc. SPIE 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 1344751 (16 January 2025); https://doi.org/10.1117/12.3045924

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KEYWORDS

Facial recognition systems

Deep learning

Data modeling

Detection and tracking algorithms

Feature extraction

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