TopoFR: a closer look at topology alignment on face recognition
Author(s)
Type
Conference Paper
Abstract
The field of face recognition (FR) has undergone significant advancements with the rise of deep learning. Recently, the success of unsupervised learning and graph neural networks has demonstrated the effectiveness of data structure information. Considering that the FR task can leverage large-scale training data, which intrinsically contains significant structure information, we aim to investigate how to encode such critical structure information into the latent space. As revealed from our observations, directly aligning the structure information between the input and latent spaces inevitably suffers from an overfitting problem, leading to a structure collapse phenomenon in the latent space. To address this problem, we propose TopoFR, a novel FR model that leverages a topological structure alignment strategy called PTSA and a hard sample mining strategy named SDE. Concretely, PTSA uses persistent homology to align the topological structures of the input and latent spaces, effectively preserving the structure information and improving the generalization performance of FR model. To mitigate the impact of hard samples on the latent space structure, SDE accurately identifies hard samples by automatically computing structure damage score (SDS) for each sample, and directs the model to prioritize optimizing these samples. Experimental results on popular face benchmarks demonstrate the superiority of our TopoFR over the state-of-the-art methods. Code and models are available at: https://github.com/modelscope/facechain/tree/main/face_module/TopoFR.
Date Issued
2025-02-01
Date Acceptance
2024-12-01
Citation
Advances in Neural Information Processing Systems, 2025, 37, pp.37213-37240
ISBN
9798331314385
ISSN
1049-5258
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Start Page
37213
End Page
37240
Journal / Book Title
Advances in Neural Information Processing Systems
Volume
37
Copyright Statement
© 2025 Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Source
NeurIPS 2024
Publication Status
Published
Start Date
2024-12-10
Finish Date
2024-12-15
Coverage Spatial
Vancouver, Canada