WFID

Abstract

We present WFID, a passive device-free indoor human identification system with one pair of WiFi signal transmitter and receiver. WFID design is motivated by the observation that PHY layer Channel State Information (CSI) is capable of capturing the frequency diversity of wideband channel, such that the human body curve may be uniquely identified by learning the feature pattern of CSI. Different from many CSI-based techniques focusing on phase shift, we propose a novel feature of subcarrier-amplitude frequency (SAF). Based on this feature, WFID realizes human identification through a linear-kernel SVM. We have implemented a prototype of WFID with a commercial AP and a computer equipped with one Intel 5300 NIC. WFID is evaluated in two typical indoor scenarios. The results confirm that WFID achieves high classification accuracy which is permanent over several days under two typical indoor scenarios, with low computation cost. This reveals the potential for WFID to realize real-time indoor human identification.