TopoFR: A Closer Look at Topology Alignment on Face Recognition

2024-10-14

Neural Information Processing Systems

AI Summary

To address insufficient latent-space structural modeling and topological collapse in face recognition, this paper proposes the Topological Alignment Framework (TAF), the first to incorporate persistent homology into latent-space structural regularization. TAF introduces Persistent Topological Structure Alignment (PTSA), which enforces cross-domain topological consistency between input and latent spaces, and designs a self-supervised Structural Damage Scoring (SDS) mechanism to guide Structural Damage-aware Example mining (SDE). By transcending conventional geometric alignment paradigms, TAF effectively mitigates structural collapse induced by overfitting. Extensive experiments on mainstream face recognition benchmarks demonstrate significant improvements over state-of-the-art methods, with enhanced topological fidelity, generalization, and robustness of the learned latent space. The code and pretrained models are publicly available.

Technology Category

• Machine Learning: Structured Learning
• Computer Vision: Other Foundations of Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Bridging structured and unstructured data

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.

Problem

• Addressing structure collapse in face recognition latent space alignment
• Preserving topological structure information to improve generalization performance
• Mitigating hard sample impact through automated structure damage scoring

Innovation

• Topological alignment using persistent homology for structure preservation
• Hard sample mining via structure damage score computation
• Preventing structure collapse in latent space with PTSA