An analysis of different approaches to gait recognition using cell phone based accelerometers

ACM International Conference Proceeding Series

Volumn , Issue , 2013, Pages 293-300

  Muaaz, Muhammad ^a   Mayrhofer, René ^a  

^a UNIVERSITY OF APPLIED SCIENCES UPPER AUSTRIA   (Austria)

Author keywords

accelerometer; Authentication; biometrics; gait recognition; machine learning

Indexed keywords

ACCELEROMETER SENSOR; AUTHENTICATION SYSTEMS; DYNAMIC TIME WARPING; GAIT RECOGNITION; PERSONAL MOBILE DEVICES; PIECEWISE LINEAR APPROXIMATIONS; SIMILARITY MEASURE; SUPPORT VECTOR MACHINE (SVMS);

ACCELEROMETERS; AUTHENTICATION; BIOMETRICS; GAIT ANALYSIS; LEARNING SYSTEMS; MOBILE COMPUTING; MOBILE DEVICES; PIECEWISE LINEAR TECHNIQUES; SUPPORT VECTOR MACHINES;

MATRIX ALGEBRA;

EID: 84897669508     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2536853.2536895     Document Type: Conference Paper

References (29)

1. The Oxford English Dictionary. Oxford University Press (Oxford UK), 4th edition, 1951.
2. H. Ailisto, M. Lindholm, J. Mäntyjärvi, E. Vildjiounaite, and S. Mäkelä. Identifying people from gait pattern with accelerometers. In Biometric Technology for Human Identification II Bd. 5779, SPIE, number 7-14, 2005.
3. D. Boswell. Introduction to support vector machines. Aug 2006.
4. F. Breitinger and C. Nickel. User survey on phone security and usage. In BIOSIG, pages 139-144, 2010.
5. L. M. A. A. Buss, A. Bazin, and M. S. Nixon. A floor sensor system for gait recognition. In Fourth IEEE Workshop on Automatic Identification Advanced Technologies, AutoID, 2005.
6. P. C. Cattin. Biometric Authentication System Using Human Gait. PhD thesis, Swiss Federal Institute of Technology, ETH, Zurich, 2002.
7. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011.
8. J. Chen and J. Ye. Training svm with indefinite kernels. In Proceedings of the 25th international conference on Machine learning, ICML '08, pages 136-143, New York, NY, USA, 2008. ACM.
9. R. T. Collins, R. Gross, and J. Shi. Silhouette-based human identification from body shape and gait. Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002,S:366-371, 2002.
10. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 1st edition, 2000.
11. F. Dadashi, B. N.Araabi, and H. Soltanian-Zadeh. Gait recognition using wavelet packet silhouette representation and transductive support vector machines. roceedings of the 2nd International Congress on Image and Signal Processing, CISP, pages 1-5, 2009.
12. M. O. Derawi. Smartphones and Biometrics: Gait and Activity Recognition. PhD thesis, GjØvik University College, November 2012.
13. J. Frank, S. Mannor, and D. Precup. Activity and gait recognition with time-delay embeddings. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI, 2010.
14. D. Gafurov. Performance and Security Analysis of Gait-based User Authentication. PhD thesis, Universitas Osloensis, 2004.
15. T. Graepel, R. Herbrich, P. Bollmann-sdorra, and K. Obermayer. Classification on Pairwise Proximity Data. In Neural Information Processing Systems, pages 438-444, 1998.
16. A. Jalalian and S. K. Chalup. GDTW-P-SVMs: Variable-length time series analysis using support vector machines. Neurocomputing, 99(0):270-282, 2013.
17. J. Jenkins and C. Ellis. Using ground reaction forces from gait analysis: Body mass as a weak biometric. Pervasive Computing, 4480/2007:251-267, 2007.
18. E. J. Keogh, S. Chu, D. Hart, and M. J. Pazzani. An online algorithm for segmenting time series. In Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM '01, pages 289-296, Washington, DC, USA, 2001. IEEE Computer Society.
19. E. J. Keogh and M. J. Pazzani. Scaling up dynamic time warping for datamining applications. In In Proc. 6th Int. Conf. on Knowledge Discovery and Data Mining, pages 285-289, 2000.
20. C. Mertens, F. Grenez, and J. Schoentgen. Preliminary evaluation of speech sample salience analysis for speech cycle detection. Proceedings 3rd Advanced Voice Function Assessment International Workshop, pages 29-32., May 2009.
21. M. Muaaz and C. Nickel. Influence of different walking speeds and surfaces on accelerometer-based biometric gait recognition. In Telecommunications and Signal Processing (TSP), 2012 35th International Conference on, pages 508-512, 2012.
22. C. Nickel. Accelerometer-based Biometric Gait Recognition for Authentication on Smartphones. PhD thesis, TU Darmstadt, June 2012.
23. C. Nickel, M. O. Derawi, P. Bours, and C. Busch. Scenario test of accelerometer-based biometric gait recognition. 3rd International Workshop on Security and Communication Networks, 2011.
24. R. J. Orr and G. D. Abowd. The smart floor: A mechanism for natural user identification and tracking. Proceedings of the Conference on Human Factors in Computing Systems, 2000.
25. H. Saevanee, N. Clarke, and S. Furnell. Multi-modal behavioural biometric authentication for mobile devices. In Information Security and Privacy Research, volume 376 of IFIP Advances in Information and Communication Technology, pages 465-474. Springer Berlin Heidelberg, 2012.
26. S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K. W. Bowyer. The humanID gait challenge problem: data sets, performance, and analysis. In IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 162-177, 2005.
27. S. Sprager and D. Zazula. A cumulant-based method for gait identification using accelerometer data with principal component analysis and support vector machine. In WSEAS TRANSACTIONS on SIGNAL PROCESSING, 2005.
28. B.-K. Yi, H. Jagadish, and C. Faloutsos. Efficient retrieval of similar time sequences under time warping. In Data Engineering, 1998. Proceedings., 14th International Conference on, pages 201-208, 1998.
29. J.-H. Yoo, D. Hwang, K.-Y. Moon, and M. S. Nixon. Automated human recognition by gait using neural network. Image Processing Theory, Tools & Applications, IPTA, pages 1-6, 2008.