AlignFace: Human-Aligned Face Similarity Metric with Interpretable Concept Relations
Computer vision models for generated facial content, such as face editing and privacy protection, increasingly affect people, requiring similarity metrics that serve as faithful proxies for human perception. While perceptual evaluation has progressed from signal-based heuristics to representation-based metrics, current approaches are limited to behavioral modeling without cognitive alignment. They rely on implicit and spurious relations while assuming a universal observer, failing to account for inherent variations across diverse human populations. This leads to inaccurate evaluative models of stakeholders and misleading guidance for generative model debugging. Rather than treating percep...
| Authors | Ying Huang, Wencan Zhang, Brian Y. Lim |
|---|---|
| Primary category | cs.MM |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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