Human identification using WiFi signal

WiFi devices are now pervasive in our environment. Recent research has demonstrated that it is possible to sense the perturbations created by human motion in the WiFi spectrum to identify basic activities, gestures and even keystrokes. In this demo, we address the yet unsolved problem of human identification using WiFi spectrum sensing. We present WiFi-ID, a device-free system that uses off-the-shelf equipment to uniquely identify individuals as they walk through urban spaces that are filled with WiFi transmissions. Our system exploits the fact that each individual has a unique walking style which causes unique disturbances in the WiFi signals. WiFi-ID analyses the Channel State Information and extracts unique features that allow us to identify individuals. We will demonstrate a functioning prototype of our system and use conference attendees as test subjects.