HiddenTag: Enabling Person Identification Without Privacy Exposure

Abstract: Person identification is the key to enable personalized services in smart homes, including the smart voice assistant, augmented reality, and targeted advertisement. Although research in the past decades in person identification has brought technologies with high accuracy, existing solutions either require explicit user interaction or rely on images and video processing, and thus suffer from cost and privacy limitations. In this paper, we introduce a device free personal identification system–HiddenTag, which utilizes smartphones to identify different users via profiling indoor activities with inaudible sound and channel state information(CSI). HiddenTag sends inaudible sound and senses its diffraction and multi-path reflection using smartphones. Based upon the multi-path effects and human body absorption, we design suitable sound signals and acoustic features for constructing the human body signatures. In addition, we use CSI to trigger the system of acoustic sensing. Extensive experiments indicate that HiddenTag can distinguish multi-person in 10–15 s with 95.1% accuracy. We implement a prototype of HiddenTag with an online system by Android smartphones and maintain 84%–90% online accuracy.

Keywords: person identification; acoustic sensing; CSI; smart home