Anonymizing Egocentric Videos

Daksh Thapar, Aditya Nigam, Chetan Arora; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2320-2329

In egocentric videos, the face of a wearer capturing the video is never captured. This gives a false sense of security that the wearer's privacy is preserved while sharing such videos. However, egocentric cameras are typically harnessed to wearer's head, and hence, also capture wearer's gait. Recent works have shown that wearer gait signatures can be extracted from egocentric videos, which can be used to determine if two egocentric videos have the same wearer. In a more damaging scenario, one can even recognize a wearer using hand gestures from egocentric videos, or identify a wearer in third person videos such as from a surveillance camera. We believe, this could be a death knell in sharing of egocentric videos, and fatal for egocentric vision research. In this work, we suggest a novel technique to anonymize egocentric videos, which create carefully crafted, but small, and imperceptible optical flow perturbations in an egocentric video's frames. Importantly, these perturbations do not affect object detection or action/activity recognition from egocentric videos but are strong enough to dis-balance the gait recovery process. In our experiments on benchmark \epic dataset, the proposed perturbation degrades the wearer recognition performance of [??], from 66.3% to 13.4%, while preserving the activity recognition performance of [??] from 89.6% to 87.4%. To test our anonymization with more wearer recognition techniques, we also developed a stronger, and more generalizable wearer recognition method based on camera egomotion cues. The approach achieves state-of-the-art (SOTA) performance of 59.67% on \epicns, compared to 55.06% by [??]. However, the accuracy of our recognition technique also drops to 12% using the proposed anonymizing perturbations.