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Attributes Shape the Embedding Space of Face Recognition Models
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Studies how human-interpretable attributes shape the embedding geometry of face recognition models, proposing a macroscale KS-test analysis on CelebA and a microscale "invariance energy" measure validated on a toy MLP, pretrained FR models, and attribute-specific fine-tuned models.
Problem
Face recognition embeddings are black boxes; it is unclear how interpretable facial and image attributes shape the geometry of the embedding space and to what degree FR models are invariant to them.
Approach
Two-scale geometric view of FR embedding spaces: a macroscale KS-statistic test of whether attribute modalities change inter-identity distance distributions, and a microscale scale-independent invariance energy computed from normalized vector fields induced by GAN-Control attribute variations, analogous to an n-vector model Hamiltonian.
Key results
- Intra-entropy of CelebA attributes correlates negatively with KS statistics for all five FR models (Spearman about -0.57 to -0.66).
- All tested pretrained FR models are most invariant to low-level attributes like contrast and illumination and least invariant to age and head angle; FaceNet shows significantly lower energy (more sensitivity) across all attributes.
- Fine-tuning on an attribute increases the invariance energy most for that same attribute in both ArcFace and AdaFace, with the diagonal maximum in the rescaled energy table.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Fine-tune on a single attribute's augmented data | ArcFace and AdaFace · Generated hold-out images with the matching attribute variation | attribute-specific invariance (parameter-free loss) | Helped |
| Fine-tune on a specific attribute rather than a different attribute | ArcFace and AdaFace · Generated data with varied attributes | invariance energy for the fine-tuned attribute | Helped |
| Fine-tune on one attribute, which can also affect other attributes | ArcFace and AdaFace · Generated data with varied attributes | attribute-specific performance | Mixed |
| Use FaceNet instead of ArcFace | Pretrained face recognition models · Synthetic identity point clouds; multiple attributes and scales | invariance energy | Hurt |
| Use AdaFace instead of ArcFace | Pretrained face recognition models · Synthetic identity point clouds; hair color and hue | invariance energy | Hurt |
| Compare attributes with different intra-identity variability | FaceNet, ArcFace, and AdaFace · CelebA | KS statistic | Mixed |
Surprising findings
- Fine-tuning on one attribute (e.g., age or brightness) can improve performance on another attribute like hair color more than on head orientation, so specialization is not fully disentangled.
Limitations
- Attribute transformations rely on GAN-Control, which offers only five pretrained controllable attributes plus low-level augmentations, and fuzzy attribute definitions (e.g., blonde hair) in CelebA.
- Macroscale KS analysis handles only discrete attributes and does not rank structure precisely; UMAP visualizations do not preserve distances.
- Fine-tuning experiments use only ArcFace and AdaFace ResNet18 and a parameter-free loss adaptation, limiting generality.
Experimental setup
Datasets: CelebA LFW MS1MV3 GAN-Control synthetic identities
Benchmarks: CelebA LFW
Metrics: Kolmogorov-Smirnov statistic Spearman correlation invariance energy parameter-free loss ROC AUC
Baselines: FaceNet ArcFace AdaFace SphereFaceR
Builds on: ArcFace AdaFace FaceNet GAN-Control
Closest papers by meaning
- Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification (AAAI 2017 · similarity 0.87)
- Representation Rigidity in Face Embeddings: Orthogonal Identifiability and Backfill-Free Compatibility (NeurIPS 2026 · similarity 0.87)
- TopoFR: A Closer Look at Topology Alignment on Face Recognition (NeurIPS 2024 · similarity 0.85)
- Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors (ICLR 2025 · similarity 0.85)
- Disentangled Representation Learning via Modular Compositional Bias (NeurIPS 2025 · similarity 0.84)
- LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition (CVPR 2024 · similarity 0.84)
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