Toward a More Ethical Facial Age Estimation: A Generalized Zero-Shot Benchmark Without Training on Children's Data
Age estimation from facial images typically relies on training data that includes images of minors, a practice that raises ethical, legal, and privacy concerns and that child-data governance frameworks explicitly advise against. While the task remains relevant (e.g., for detecting child sexual abuse imagery), we advocate against using data from minors entirely and quantify what the exclusion costs in accuracy. We formalize age estimation without children's training data as a generalized zero-shot learning (GZSL) problem: age intervals present during training are seen classes and withheld intervals are unseen, with models evaluated jointly on both. The generalized setting, rather than conv...
| Authors | Caio Petrucci, Leo Sampaio Ferraz Ribeiro, Sandra Avila |
|---|---|
| Primary category | cs.CV |
| Published | 28 May 2026 |
| Last updated at source | 30 July 2026 |
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