User identification based on conversational gestures

Virtual reality relies on tracking to provide users with a compelling and comfortable experience. However, such VR tracking increases the amount of data we generate about ourselves when online. In this work, we investigate the extent to which social gestures can be used to identify us. Using a dataset of 11 speakers, collected with motion capture, we train a fully convolutional network with features that emulate the motion tracking done by virtual reality systems. We find high identification accuracy of up to 99%, even with very short clips (4 seconds).