Hand Movement, Orientation, and Grasp (HMOG) is a set of behavioral features to
continuously authenticate smartphone users. HMOG features unobtrusively capture
subtle micro-movement and orientation dynamics resulting from how a user
grasps, holds, and taps on the smartphone. In this project, we evaluated
authentication and biometric key generation (BKG) performance of HMOG features
on data collected from 100 subjects typing on a virtual keyboard.
Data was collected under two conditions: sitting and walking. We achieved
authentication EERs as low as 7.16% (walking) and 10.05% (sitting) when we
combined HMOG, tap, and keystroke features. We performed experiments to
investigate why HMOG features perform well during walking. Our results suggest
that this is due to the ability of HMOG features to capture distinctive body
movements caused by walking, in addition to the hand-movement dynamics from
taps.
With BKG, we achieved EERs of 15.1% using HMOG combined with taps. In
comparison, BKG using tap, key hold, and swipe features had EERs between 25.7%
and 34.2%. We also analyzed the energy consumption of HMOG feature extraction
and computation. Our analysis shows that HMOG features extracted at 16Hz sensor
sampling rate incurred a minor overhead of 7.9% without sacrificing
authentication accuracy.
The paper reporting on HMOG was published in the IEEE Transactions on
Information Forensics and Security, and is available
here. The team’s work on HMOG was also
presented as a poster at SenSys 2014. The poster is available
here. The HMOG dataset can be
downloaded from here.
This project is funded by DARPA.