An Egocentric Look at Video Photographer Identity

TL;DR

This paper demonstrates that egocentric videos contain unique identity cues from camera motion, enabling reliable recognition of the photographer with high accuracy, thus challenging assumptions of anonymity in head-worn videos.

Contribution

It introduces a method to identify photographers from egocentric videos using camera motion analysis, achieving over 90% accuracy, revealing privacy implications.

Findings

Camera motion provides reliable identity information.

Photographer recognition accuracy exceeds 90%.

Sharing egocentric videos compromises photographer anonymity.

Abstract

Egocentric cameras are being worn by an increasing number of users, among them many security forces worldwide. GoPro cameras already penetrated the mass market, reporting substantial increase in sales every year. As head-worn cameras do not capture the photographer, it may seem that the anonymity of the photographer is preserved even when the video is publicly distributed. We show that camera motion, as can be computed from the egocentric video, provides unique identity information. The photographer can be reliably recognized from a few seconds of video captured when walking. The proposed method achieves more than 90% recognition accuracy in cases where the random success rate is only 3%. Applications can include theft prevention by locking the camera when not worn by its rightful owner. Searching video sharing services (e.g. YouTube) for egocentric videos shot by a specific photographer may also become possible. An important message in this paper is that photographers should be aware that sharing egocentric video will compromise their anonymity, even when their face is not visible.