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

作     者：Xinyu Yang Runhan Li Xindi Yang Yong Zhou Yi Liu Jing-Dong J.Han 

作者机构：School of Life SciencesPeking UniversityBeijing 100871China Peking-Tsinghua Center for Life SciencesAcademy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology(CQB)Peking UniversityBeijing 100871China Beijing Key Lab of Traffic Data Analysis and MiningSchool of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100044China Clinical Research InstituteShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China 

出 版 物：《Science China(Life Sciences)》 (中国科学（生命科学英文版）)

年 卷 期：2024年第67卷第7期

页      面：1489-1501页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(92049302,92374207,32088101,32330017) the National Key Research and Development Program of China(2020YFA0804000)。 

主　　题：face point cloud age estimation face verification privacy coordinate-wise monotonic transformation 

摘      要：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 computational3D 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.