Whose Face Is It Anyway? A Multi-Model Audit of Facial Affect Recognition on Children, and Why the Gap Is the Head, Not the Features

The paper audits five AffectNet‑pretrained facial affect models on child faces, revealing that all models perform worse on naturalistic child images and consistently confuse fear with surprise. The degradation is linked to specific expression morphologies—particularly open‑mouth faces—and to South‑Asian children, rather than skin tone. A linear probe shows that most of the gap lies in the classifier head, and fine‑tuning just this head on a small amount of child data improves performance by up to 28% on the largest child datasets.

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