Article

Unobtrusive multimodal biometrics for ensuring privacy and information security with personal devices

Authors:

Elena Vildjiounaite,

Satu-Marja Mäkelä,

Mikko Lindholm,

Reima Riihimäki,

Vesa Kyllönen,

Jani Mäntyjärvi,

Heikki AilistoAuthors Info & Claims

PERVASIVE'06: Proceedings of the 4th international conference on Pervasive Computing

Pages 187 - 201

https://doi.org/10.1007/11748625_12

Published: 07 May 2006 Publication History

Abstract

The need for authenticating users of ubiquitous mobile devices is becoming ever more critical with the increasing value of information stored in the devices and of services accessed via them. Passwords and conventional biometrics such as fingerprint recognition offer fairly reliable solutions to this problem, but these methods require explicit user authentication and are used mainly when a mobile device is being switched on. Furthermore, conventional biometrics are sometimes perceived as privacy threats. This paper presents an unobtrusive method of user authentication for mobile devices in the form of recognition of the walking style (gait) and voice of the user while carrying and using the device. While speaker recognition in noisy conditions performs poorly, combined speaker and accelerometer-based gait recognition performs significantly better. In tentative tests with 31 users the Equal Error Rate varied between 2% and 12% depending on noise conditions, typically less than half of the Equal Error Rates of individual modalities.

References

[1]

http://news.bbc.co.uk/1/hi/uk/1748258.stm

[2]

Miller, A., PDA security concerns, Network Security, 2004 (7, July): p. 8-10

Digital Library

[3]

Johnson, M., Biometrics and the Threat to Civil Liberties, IEEE Computer, 2004, 37(4), p. 90-92.

Digital Library

[4]

Bimbot F, Bonastre J-F., Fredoulle C., Gravier G., Magrin-Chagnolleay I., Meignier S., Merlin T., Ortega-Garcia J., Petrovska-Delacretaz D and Reynolds D. A., A tutorial on text-independent Speaker Verfication. Eurasip Jasp 2004:4 (2004) pp. 430-451.

Digital Library

[5]

Przybocki M., Martin A., NIST's Assessment of Text Independent Speaker Recognition Performance, The Advent of Biometircs on the Internet, A COST 275 Workshop in Rome, Italy, Nov. 7-8 2002.

[6]

Campbell, J. P., Reynolds, D. A., Dunn, R. B., Fusing High- and Low-Level Features for Speaker Recognition, In Proc. Eurospeech in Geneva, Switzerland, ISCA, pp. 2665-2668.

[7]

Yoma, N.B.; Villar, M.; Speaker verification in noise using a stochastic version of the weighted Viterbi algorithm Speech and Audio Processing, IEEE Transactions on Volume 10, Issue 3, March 2002 Page(s):158-166.

[8]

Hu Guangrui; Wei Xiaodong; Improved robust speaker identification in noise using auditory properties Intelligent Multimedia, Video and Speech Processing, 2001. Proceedings of 2001 International Symposium on 2-4 May 2001 Page(s):17-19.

[9]

Drygajlo, A.; El-Maliki, M.; Speaker verification in noisy environments with combined spectral subtraction and missing feature theory, ICASSP '98. Proceedings of the 1998 IEEE International Conference on Volume 1, 12-15 May 1998 Page(s):121-124 vol.1.

[10]

Niyogi, S.A., Adelson, E.H. Analyzing and recognizing walking gures in XYT. in Conference of Computer Vision and Pattern Recognition. 1994. Seattle, WA.

[11]

BenAbdelkader, C., Cutler, R., Nanda, H., Davis, L.S. EigenGait: Motion-based Recognition of People using Image Self-similarity. in Intl Conf. on Audio and Video-based Person Authentication (AVBPA). 2001.

Digital Library

[12]

Nixon, M., Carter, J., Shutler, J., Grant, M., New Advances in Automatic Gait Recognition, Information Security Technical Report, 2002. 7(4): p. 23-35.

[13]

Wang, L., Tan, T., Hu, W., Ning, H., Automatic gait recognition based on statistical shape analysis, IEEE Trans. Image Processing, 2003, 12(9): p. 120-1131.

Digital Library

[14]

Bianchi, L., Angelini, D., Lacquaniti, F., Individual characteristics of human walking mechanics. Eur. J. Physiol, 1998, 436: p. 343-356.8.

[15]

Bolle, R.M., Connell, J.H., Pankanti, S., Ratha, N.K., Senior, A.W., Guide to Biometrics. 2004, New York: Springer. 365.

Digital Library

[16]

Heinz, E., Kunze, K., Sulistyo, S., Junker, H., Lukowicz, P., Tröster, G., Experimental Evaluation of Variations in Primary Features Used for Accelerometric Context Recognition, Proceedings of Europian Symposium on Ambient Intelligence (EUSAI 2003), pp. 252-263.

[17]

J. Lester, Hannaford, B., Borriello, G., "Are You with Me?" - Using Accelerometers to Determine if Two Devices are Carried by the Same Person," presented at 2nd Int. Conf. on Pervasive Computing, Linz, Austria, 2004.

[18]

Rragami, L., Gifford, M., Edwards, N., DSV - Questions remain., Biometric Technology Today, 2003. 11(11): p. 7.

[19]

Sang, L., Wu, Z., Yang, Y. Speaker recognition system in multi-channel environment. in IEEE International Conference on Systems, Man and Cybernetics, System Security and Assurance. 2003. Washington, DC: IEEE Press.

[20]

Ailisto, H., Haataja, V., Kyllönen, V., Lindholm, M. Wearable Context Aware Terminal for Maintenance Personnel. in Ambient Intelligence, First European Symposium, EUSAI 2003. 2003. Veldhoven, The Netherlands: Springer.

[21]

Mainguet, J.-F., Biometrics for large-scale consumer products, in International Conference on Artificial Intelligence IC-AI 2003. 2003.

[22]

Bohn, J., Coroama, V., Langheinrich, M., Mattern, F., Rohs, M., (2005) Social, Economic, and Ethical Implications of Ambient Intelligence and Ubiquitous Computing, In: W. Weber, J. Rabaey, E. Aarts (Eds.): Ambient Intelligence. Springer-Verlag, pp. 5-29.

[23]

Hazen, T., Weinstein, E., Kabir, R., Park A., Heisele, B., Multi-Modal Face and Speaker Identification on a Handheld Device, In Proceedings of the Workshop on Multimodal User Authentication, pp. 113-120, Santa Barbara, California, December, 2003.

[24]

Lee B, Hasegawa-Johnson M, Goudeseune C, Kamdar S, Borys S, Liu M, Huang T., "AVICAR: Audio-Visual Speech Corpus in a Car Environment", INTERSPEECH2004- ICSLP Jeju Island, Korea, October 2004.

[25]

Ailisto, H.; Lindholm, M.; Mäntyjärvi, J.; Vildjiounaite, E.; Mäkelä, S.-M., Identifying people from gait pattern with accelerometers, SPIE, Vol. 5779, Biometric Technology for Human Identification II, Anil K. Jain & Nalini K. Ratha (Eds.), pp. 7-14.

[26]

Jain, A., Ross, A., Multibiometric Systems, Communications of the ACM, Special Issue on Multimodal Interfaces, Vol. 47, No. 1, pp. 34-40, January 2004.

Digital Library

[27]

State-of-the-Art Report on Multimodal Biometric Fusion, http://www.biosec.org/index.php

[28]

Gellersen, H.-W., Schmidt, A., Beigl, M., Multi-sensor context-awareness in mobile devices and smart artifacts, Mobile Networks and Applications, 7, 341-351, 2002.

Digital Library

[29]

http://www.bas.uni-muenchen.de/Bas/SV/

[30]

NTT-AT Ambient Noise Database: http://www.ntt-at.com/products_e/noise-DB/

[31]

Mäntyärvi, J., Kallio, S., Korpipää, P., Kela, J., Plomp, J., Gesture Interaction for Small Handheld Devices to Support Multimedia Applications, In Journal of Mobile Multimedia, Rinton Press, Vol.1(2), pp. 92-112, 2005.

Digital Library

Cited By

Mayrhofer RSigg S(2021)Adversary Models for Mobile Device AuthenticationACM Computing Surveys10.1145/347760154:9(1-35)Online publication date: 8-Oct-2021
https://dl.acm.org/doi/10.1145/3477601
Marsico MMecca A(2019)A Survey on Gait Recognition via Wearable SensorsACM Computing Surveys10.1145/334029352:4(1-39)Online publication date: 30-Aug-2019
https://dl.acm.org/doi/10.1145/3340293
(2018)A Survey on Gait RecognitionACM Computing Surveys10.1145/323063351:5(1-35)Online publication date: 29-Aug-2018
https://dl.acm.org/doi/10.1145/3230633
Show More Cited By

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Unobtrusive multimodal biometrics for ensuring privacy and information security with personal devices
1. Computing methodologies
  1. Machine learning

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

PERVASIVE'06: Proceedings of the 4th international conference on Pervasive Computing

May 2006

2 pages

ISBN:3540338942

Editors:
Kenneth P. Fishkin
Google, Inc., 720 4th Ave. #400, Kirkland, WA
,
Bernt Schiele
Computer Science Department, TU Darmstadt, 720 4th Ave. #400, Kirkland, WA
,
Paddy Nixon
System Research Group, School of Computer Science and Informatics, UCD, 720 4th Ave. #400, Dublin, WA, Ireland
,
Aaron Quigley
Imaging, Visualisation & Graphics Lab, Systems Research Group, School of Computer Science & Informatics, University College Dublin, 720 4th Ave. #400, Dublin 4, WA, Ireland

Sponsors

SFI: Science Foundation Ireland
LERO: The Irish Software Engineering Research Centre
Intel Research
UCD: University College Dublin
Intel Ireland: Intel Ireland

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 07 May 2006

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Cited By

Mayrhofer RSigg S(2021)Adversary Models for Mobile Device AuthenticationACM Computing Surveys10.1145/347760154:9(1-35)Online publication date: 8-Oct-2021
https://dl.acm.org/doi/10.1145/3477601
Marsico MMecca A(2019)A Survey on Gait Recognition via Wearable SensorsACM Computing Surveys10.1145/334029352:4(1-39)Online publication date: 30-Aug-2019
https://dl.acm.org/doi/10.1145/3340293
(2018)A Survey on Gait RecognitionACM Computing Surveys10.1145/323063351:5(1-35)Online publication date: 29-Aug-2018
https://dl.acm.org/doi/10.1145/3230633
Xu WShen YZhang YBergmann NHu W(2017)Gait-WatchProceedings of the Second International Conference on Internet-of-Things Design and Implementation10.1145/3054977.3054991(59-70)Online publication date: 18-Apr-2017
https://dl.acm.org/doi/10.1145/3054977.3054991
Eberz SRasmussen KLenders VMartinovic IKarri RSinanoglu OSadeghi AYi X(2017)Evaluating Behavioral Biometrics for Continuous AuthenticationProceedings of the 2017 ACM on Asia Conference on Computer and Communications Security10.1145/3052973.3053032(386-399)Online publication date: 2-Apr-2017
https://dl.acm.org/doi/10.1145/3052973.3053032
Blasco JChen TTapiador JPeris-Lopez P(2016)A Survey of Wearable Biometric Recognition SystemsACM Computing Surveys10.1145/296821549:3(1-35)Online publication date: 16-Sep-2016
https://dl.acm.org/doi/10.1145/2968215
Al Abdulwahid AClarke NStengel IFurnell SReich C(2016)Continuous and transparent multimodal authenticationCluster Computing10.1007/s10586-015-0510-419:1(455-474)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1007/s10586-015-0510-4
Xu WSong JAbdelzaher TMascolo C(2015)Mobile Applications Based on Smart Wearable DevicesProceedings of the 13th ACM Conference on Embedded Networked Sensor Systems10.1145/2809695.2822525(505-506)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1145/2809695.2822525
Lu HHuang JSaha TNachman LDunne LMartin TBeigl M(2014)Unobtrusive gait verification for mobile phonesProceedings of the 2014 ACM International Symposium on Wearable Computers10.1145/2634317.2642868(91-98)Online publication date: 13-Sep-2014
https://dl.acm.org/doi/10.1145/2634317.2642868
Ngo TMakihara YNagahara HMukaigawa YYagi Y(2014)The largest inertial sensor-based gait database and performance evaluation of gait-based personal authenticationPattern Recognition10.1016/j.patcog.2013.06.02847:1(228-237)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1016/j.patcog.2013.06.028
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