GaitLock : Protect Virtual and Augmented Reality Headsets Using Gait

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

14 Scopus Citations
View graph of relations

Author(s)

  • Yiran Shen
  • Hongkai Wen
  • Chengwen Luo
  • Tao Zhang
  • Wen Hu
  • Daniela Rus

Detail(s)

Original languageEnglish
Article number8276563
Pages (from-to)484-497
Number of pages14
Journal / PublicationIEEE Transactions on Dependable and Secure Computing
Volume16
Issue number3
Online published31 Jan 2018
Publication statusPublished - May 2019
Externally publishedYes

Abstract

With the fast penetration of commercial Virtual Reality (VR) and Augmented Reality (AR) systems into our daily life, the security issues of those devices have attracted significant interests from both academia and industry. Modern VR/AR systems typically use head-mounted devices (i.e., headsets) to interact with users, and often store private user data, e.g., social network accounts, online transactions or even payment information. This poses significant security threats, since in practice the headset can be potentially obtained and accessed by unauthenticated parties, e.g., identity thieves, and thus cause catastrophic breach. In this paper, we propose a novel GaitLock system, which can reliably authenticate users using their gait signatures. Our system doesn't require extra hardware, e.g., fingerprint sensors or retina scanners, but only uses the on-board inertial measurement units (IMUs) equipped in almost all mainstream VR/AR headsets to authenticate the legitimate users from intruders, by simply asking them to walk a few steps. To achieve that, we propose a new gait recognition model Dynamic-SRC, which combines the strength of Dynamic Time Warping (DTW) and Sparse Representation Classifier (SRC), to extract unique gait patterns from the inertial signals during walking. We implement GaitLock on Google Glass (a typical AR headset), and extensive experiments show that GaitLock outperforms the state-of-the-art systems significantly in recognition accuracy (>>98 percent success in 5 steps), and is able to run in-situ on the resource-constrained VR/AR headsets without incurring high energy cost.

Research Area(s)

  • dynamic time warping, Gait recognition, sparse representation classification, VR/ AR

Citation Format(s)

GaitLock : Protect Virtual and Augmented Reality Headsets Using Gait. / Shen, Yiran; Wen, Hongkai; Luo, Chengwen; Xu, Weitao; Zhang, Tao; Hu, Wen; Rus, Daniela.

In: IEEE Transactions on Dependable and Secure Computing, Vol. 16, No. 3, 8276563, 05.2019, p. 484-497.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review