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Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
March 20, 2024, 4:45 a.m. | Yuxi Mi, Zhizhou Zhong, Yuge Huang, Jiazhen Ji, Jianqing Xu, Jun Wang, Shaoming Wang, Shouhong Ding, Shuigeng Zhou
cs.CV updates on arXiv.org arxiv.org
Abstract: The widespread adoption of face recognition has led to increasing privacy concerns, as unauthorized access to face images can expose sensitive personal information. This paper explores face image protection against viewing and recovery attacks. Inspired by image compression, we propose creating a visually uninformative face image through feature subtraction between an original face and its model-produced regeneration. Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation. …
arxiv cs.cv face face recognition feature privacy recognition type
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