Egocentric cameras are being worn by an increasing number of users, among them many security forces world-wide. GoPro cameras already penetrated the mass market, and Google Glass may follow soon. As head-worn cam-eras do not capture the face and body of the wearer, it may seem that the anonymity of the wearer can be preserved even when the video is publicly distributed. We show that motion features in egocentric video pro-vide biometric information, and the identity of the user can be determined quite reliably from a few seconds of video. Biometrics are extracted by training Convolutional Neural Network (CNN) architectures on coarse optical flow. Egocentric video biometrics can prevent theft of wear-able cameras by locking the camera when worn by people other than the owner. In video sharing services, this Bio-metric measure can help to locate automatically all videos shot by the same user. An important message in this paper is that people should be aware that sharing egocentric video will compromise their anonymity. 1.