HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

TL;DR

This paper introduces HMOG, a set of behavioral biometric features based on hand movements, orientation, and grasp, for continuous smartphone user authentication and biometric key generation, demonstrating high accuracy and low energy overhead.

Contribution

The paper presents a comprehensive evaluation of HMOG features and their combinations for authentication and key generation, including energy consumption analysis, which is novel in this context.

Findings

Achieved low EERs of 7.16% (walking) and 10.05% (sitting) for authentication.

HMOG features combined with taps improve biometric key generation EERs to 15.1%.

HMOG features incur only 7.9% energy overhead at 16Hz sampling rate.

Abstract

We introduce Hand Movement, Orientation, and Grasp (HMOG), 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. 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. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions, and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).