ArticleScience China. Life sciences2024
Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud.
Article in Science China. Life sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 2 citations in OpenAlex.
- Structural decomposition enables multi-omics dissection of common and organ-specific aging.Science China. Life sciences · 2026Article
- Generation of Face Privacy-Protected Images Based on the Diffusion Model.Entropy (Basel, Switzerland) · 2024Article
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6 authors at 4 institutions in 1 country.
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Abstract
The human face is a valuable biomarker of aging, but the collection and use of its image raise significant privacy concerns. Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations. We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability. We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations, indicating that the relative positioning of facial information is a low-level biomarker of facial aging. Through visual perception tests and computational 3D face verification experiments, we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines, except when only the face shape information is accessible. Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.
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