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Align then clip: Refining graph for face clustering.

Yanlun Tu¹, Guoliang Cao², Jialiang Shen²

¹AGI Institute, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China.

Neural Networks : the Official Journal of the International Neural Network Society

|December 23, 2025

Summary

This study introduces AtC, a novel framework for face clustering that refines graph structures to improve accuracy. It enhances robustness to noise and improves scalability for large-scale face recognition datasets.

Keywords:

Face clustering Graph neural network Representation learning

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Face clustering is vital for annotating large face recognition datasets efficiently.
Current graph neural network (GNN) methods struggle with noisy graph structures from kNN graph construction.
Limitations include sensitivity to hyperparameters and noise propagation in GNNs.

Purpose of the Study:

To propose AtC (Align then Clip), a novel framework to refine graph structures for improved face clustering.
To enhance the robustness and scalability of face clustering methods.
To address limitations of conventional kNN-based graph construction in GNNs.

Main Methods:

Developed AtC framework with dual-phase optimization for graph refinement.
Introduced a distribution alignment branch during training to align noisy and clean graph representations.
Implemented Post-Clipping for adaptive edge pruning and Post-Linkage for mitigating cluster fragmentation.

Main Results:

Demonstrated scalability on datasets up to 5.21 million samples.
Showcased robustness to 30% false-positive noise in graph construction.
Achieved superior cross-domain generalization, indicating broad applicability.

Conclusions:

AtC framework significantly improves face clustering performance by refining graph structures.
The method offers enhanced robustness, scalability, and generalization for real-world face recognition tasks.
AtC provides a promising solution for efficient annotation of large-scale unlabeled face datasets.