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 ...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.展开更多
动态人脸跟踪过程中,现有的跟踪算法存在快速运动、遮挡和频繁进出摄像机视野下无法及时判定跟踪漂移导致跟踪失败,而目标再出现时作为新的目标进行跟踪.针对以上难题,提出一种融合跟踪校验和深度学习识别辅助的动态人脸跟踪算法(Kernel...动态人脸跟踪过程中,现有的跟踪算法存在快速运动、遮挡和频繁进出摄像机视野下无法及时判定跟踪漂移导致跟踪失败,而目标再出现时作为新的目标进行跟踪.针对以上难题,提出一种融合跟踪校验和深度学习识别辅助的动态人脸跟踪算法(Kernelized correlation filter with verification and recognition,KCFVR).跟踪算法核心是结合核相关滤波框架,通过跟踪校验算法判定人脸目标是否跟踪漂移导致跟踪失败;在目标重新出现时,结合深度学习网络识别辅助方法判定是否为新目标.实验结果表明:跟踪校验算法及时减少跟踪误差积累,识别辅助算法在跟踪成功率及识别精度上,都取得较优的实验结果,实现同一人脸目标的实时、持续跟踪.展开更多
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)。
文摘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.
文摘动态人脸跟踪过程中,现有的跟踪算法存在快速运动、遮挡和频繁进出摄像机视野下无法及时判定跟踪漂移导致跟踪失败,而目标再出现时作为新的目标进行跟踪.针对以上难题,提出一种融合跟踪校验和深度学习识别辅助的动态人脸跟踪算法(Kernelized correlation filter with verification and recognition,KCFVR).跟踪算法核心是结合核相关滤波框架,通过跟踪校验算法判定人脸目标是否跟踪漂移导致跟踪失败;在目标重新出现时,结合深度学习网络识别辅助方法判定是否为新目标.实验结果表明:跟踪校验算法及时减少跟踪误差积累,识别辅助算法在跟踪成功率及识别精度上,都取得较优的实验结果,实现同一人脸目标的实时、持续跟踪.
