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User evaluation of lightweight user authentication with a single tri-axis accelerometer
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ACM International Conference Proceeding Series archive
Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services table of contents
Bonn, Germany
SESSION: Safe and sound table of contents
Article No. 15  
Year of Publication: 2009
ISBN:978-1-60558-281-8
Authors
Jiayang Liu  Rice University, Houston, TX
Lin Zhong  Rice University, Houston, TX
Jehan Wickramasuriya  Motorola Inc., Schaumburg, IL
Venu Vasudevan  Motorola Inc., Schaumburg, IL
Sponsors
SIGCHI : Specialist Interest Group in Computer-Human Interaction of the ACM
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

We report a series of user studies that evaluate the feasibility and usability of light-weight user authentication with a single tri-axis accelerometer. We base our investigation on uWave, a state-of-the-art recognition system for user-created free-space manipulation, or gestures. Our user studies address two types of user authentication: non-critical authentication (or identification) for a user to retrieve privacy-insensitive data; and critical authentication for protecting privacy-sensitive data. For non-critical authentication, our evaluation shows that uWave achieves high recognition accuracy (98%) and its usability is comparable with text ID-based authentication. Our results also highlight the importance of constraints for users to select their gestures. For critical authentication, the evaluation shows uWave achieves state-of-the-art resilience to attacks with 3% false positives and 3% false negatives, or 3% equal error rate. We also show that the equal error rate increases to 10% if the attackers see the users performing their gestures. This shows the limitation of gesture-based authentication and highlights the need for visual concealment.


REFERENCES

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1
J. Liu, Z. Wang, L. Zhong, J. Wickramasuriya, and V. Vasu-devan, "uWave: Accelerometer-based Personalized Gesture Recognition and Its Applications," in Proc. IEEE Int. Conf. Pervasive Computing and Communication (PerCom), 2009.
 
2
F. G. Hofmann, P. Heyer, and G. Hommel, "Velocity Profile Based Recognition of Dynamic Gestures with Discrete Hidden Markov Models.," in Proc. Int. Wrkshp. Gesture and Sign Language in Human-Computer Interaction, 1997.
 
3
I. J. Jang and W. B. Park, "Signal Processing of the Accelerometer for Gesture Awareness on Handheld Devices," in Proc. IEEE Int. Wkshp. Robot and Human Interactive Communication, W. B. Park, Ed., 2003, pp. 139--144.
 
4
J. Kela, P. Korpipää, J. Mäntyjärvi, S. Kallio, G. Savino, L. Jozzo, and D. Marca, "Accelerometer-based gesture control for a design environment," Personal Ubiquitous Computing, vol. 10, pp. 285--299, 2006.
 
5
B. D. Payne and W. K. Edwards, "A Brief Introduction to Usable Security," IEEE Internet Computing, vol. 12, pp. 13--21, 2008.
 
6
D. Maltoni, Handbook of fingerprint recognition: Springer, 2003.
 
7
W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face recognition: A literature survey," ACM Computing Surveys, vol. 35, pp. 399--458, 2003.
 
8
R. P. Wildes, "Iris Recognition: an Emerging Biometric Technology," Proc. IEEE, vol. 85, pp. 1348--1363, 1997.
 
9
J. P. Campbell, Jr., "Speaker Recognition: a Tutorial," Proc. of the IEEE, vol. 85, pp. 1437--1462, 1997.
 
10
D. Impedovo and G. Pirlo, "Automatic signature verification: the state of the art," IEEE Trans. on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 38, pp. 609--635, 2008.
 
11
F. Okumura, A. Kubota, Y. Hatori, K. Matsuo, M. Hashimoto, and A. Koike, "A Study on Biometric Authentication Based on Arm Sweep Action with Acceleration Sensor," in Proc. Int. Symp. Intelligent Signal Processing and Communications, 2006.
 
12
E. Farella, S. O'Modhrain, L. Benini, and B. Riccó, "Gesture Signature for Ambient Intelligence Applications: A Feasibility Study," in Proc. Int. Conf. Pervasive Computing (Pervasive), 2006.
 
13
K. Matsuo, F. Okumura, M. Hashimoto, S. Sakazawa, and Y. Hatori, "Arm Swing Identification Method with Template Update for Long Term Stability," in Proc. Int. Biometrics, 2007.
 
14
J. Mantyjarvi, M. Lindholm, E. Vildjiounaite, S. M. Makela, and H. A. Ailisto, "Identifying Users of Portable Devices from Gait Pattern with Accelerometers," in Proc. of IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, 2005, pp. ii/973-ii/976 Vol. 2.
 
15
K. Hinckley, "Synchronous Gestures for Multiple Persons and Computers," in Proc. ACM Symp. User Interface Software and Technology (UIST), 2003.
 
16
R. Mayrhofer and H. Gellersen, "Shake Well Before Use: Authentication Based on Accelerometer Data," in Proc. Int. Conf. Pervasive Computing (Pervasive), 2007.
 
17
S. N. Patel, J. S. Pierce, and G. D. Abowd, "A Gesture-based Authentication Scheme for Untrusted Public Terminals," in Proc. ACM Symp. on User Interface Software and Technology (UIST), 2004.
 
18
D. Kirovski, M. Sinclair, and D. Wilson, "The Martini Synch: Joint Fuzzy Hashing Via Error Correction," in Proc. European Wrkshp. Security and Privacy in Ad-hoc and Sensor Networks, 2007.
 
19
L. E. Holmquist, F. Mattern, B. Schiele, P. Alahuhta, M. Beigl, and H.-W. Gellersen, "Smart-Its Friends: A Technique for Users to Easily Establish Connections between Smart Artefacts," in Proc. Int. Conf. Ubiquitous Computing. Atlanta, Georgia: Springer-Verlag, 2001.
 
20
D. L. Clason and T. J. Dormody, "Analyzing Data Measured by Individual Likert-Type Items," Journal of Agricultural Education, vol. 35, No. 4, pp. 31--35, 1994.
 
21
J. Cohen, P. Cohen, S. G. West, and L. S. Aiken, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences: L. Erlbaum Associates Mahwah, NJ, 2003.