首页|Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud

Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud

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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.

face point cloudage estimationface verificationprivacycoordinate-wise monotonic transformation

Xinyu Yang、Runhan Li、Xindi Yang、Yong Zhou、Yi Liu、Jing-Dong J.Han

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School of Life Sciences,Peking University,Beijing 100871,China

Peking-Tsinghua Center for Life Sciences,Academy for Advanced Interdisciplinary Studies,Center for Quantitative Biology(CQB),Peking University,Beijing 100871,China

Beijing Key Lab of Traffic Data Analysis and Mining,School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China

Clinical Research Institute,Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai 200025,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Key Research and Development Program of China

920493029237420732088101323300172020YFA0804000

2024

10.1007/s11427-023-2518-8

中国科学:生命科学(英文版)
中国科学院

中国科学:生命科学(英文版)

CSTPCD
影响因子：0.806
ISSN：1674-7305
年,卷(期)：2024.67(7)