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@article{1316101,
author = {Sitová, Zdeňka and Šeděnka, Jaroslav and Yang, Qing and Peng, Ge and Zhou, Gang and Gasti, Paolo and Balagani, Kiran and Peng, Ge},
article_number = {5},
doi = {http://dx.doi.org/10.1109/TIFS.2015.2506542},
keywords = {Behavioral biometrics; HMOG; biometric key generation; continuous authentication; energy evaluation},
language = {eng},
issn = {1556-6013},
journal = {IEEE Transactions on Information Forensics and Security},
title = {HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7349202},
volume = {11},
year = {2016}
}
TY - JOUR
ID - 1316101
AU - Sitová, Zdeňka - Šeděnka, Jaroslav - Yang, Qing - Peng, Ge - Zhou, Gang - Gasti, Paolo - Balagani, Kiran - Peng, Ge
PY - 2016
TI - HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users
JF - IEEE Transactions on Information Forensics and Security
VL - 11
IS - 5
SP - 877 - 892
EP - 877 - 892
SN - 15566013
KW - Behavioral biometrics
KW - HMOG
KW - biometric key generation
KW - continuous authentication
KW - energy evaluation
UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7349202
L2 - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7349202
N2 - 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 were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (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 the 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 a 16-Hz 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).
ER -
SITOVÁ, Zdeňka, Jaroslav ŠEDĚNKA, Qing YANG, Ge PENG, Gang ZHOU, Paolo GASTI and Kiran BALAGANI. HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users. \textit{IEEE Transactions on Information Forensics and Security}. vol.~11, No~5, p.~877 - 892. ISSN~1556-6013. doi:10.1109/TIFS.2015.2506542. 2016.
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