Privacy-Phys : facial video-based physiological modification for privacy protection
|Author:||Sun, Zhaodong1; Li, Xiaobai1|
1Center for Machine Vision and Signal Analysis, University of Oulu, 90014 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022082456044
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-08-24
The invisible remote photoplethysmography (rPPG) signals in facial videos can reveal the cardiac rhythm and physiological status. Recent studies show that rPPG is a non-contact way for emotion recognition, disease detection, and biometric identification, which means there is a potential privacy problem about physiological information leakage from facial videos. Therefore, it is essential to process facial videos to prevent rPPG extraction in privacy-sensitive situations such as online video meetings. In this letter, we propose Privacy-Phys, a novel method based on a pre-trained 3D convolutional neural network, to modify rPPG in facial videos for privacy protection. Our experimental results show that our approach can modify rPPG signals in facial videos more effectively and efficiently than the previous baseline. Our method can be applied to process facial videos in online video meetings or video-sharing platforms to prevent rPPG from being captured maliciously.
IEEE signal processing letters
|Pages:||1507 - 1511|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work was supported in part by the Academy of Finland under Grants 323287 and
345948, and in part by the Finnish Work Environment Fund under Grant 200414.
|Academy of Finland Grant Number:||
323287 (Academy of Finland Funding decision)
345948 (Academy of Finland Funding decision)
© The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0.