Individual Recognition from Gait Using Feature Value Method

Zhang, Tianxiang; Venture, Gentiane

doi:10.2478/cait-2012-0024

Abstract

We propose a novel framework to recognize individuals from gait, in order to improve HRI. We collected the motion data of the torso from 13 persons’ gait, using 2 IMU sensors. We developed Feature Value Method which is a PCA based classifier and we achieved an average individual recognition rate of 94% through cross-validation.

References

1. Loula, F., et al. Recognizing People from Their Movement. - J. of Experimental Psychology: Human Perception and Performance, Vol. 31, 2005, No 1, 210-220.10.1037/0096-1523.31.1.210
Search in Google Scholar
2. Jokisch, D., et al. Self-Recognition Versus Recognition of Others by Biological Motion: Viewpoint-Dependent Effects. - Perception, 35, 2006, 911-920.10.1068/p5540
Search in Google Scholar
3. Cutting, J., et al. Recognizing Friends by Their Walk: Gait Perception Without Familiarity Cues. - Bulletin of Psychonomic Society, Vol. 9, 1977, No 5, 353-356.10.3758/BF03337021
Search in Google Scholar
4. Jain, A., et al. Biometrics. - In: Biometrics-Personal Identification in Networked Society.Springer US, 2002.
Search in Google Scholar
5. Nixon, M. Gait Biometrics. - Biometric Technology Today, Vol. 16, 2008, No 7-8, 8-9.10.1016/S0969-4765(08)70103-6
Search in Google Scholar
6. Murray, P., et al. Walking Patterns of Normal Men. - J. of Bone And Joint Surgery, Vol. 46, 1964, No 2, 335-360.10.2106/00004623-196446020-00009
Search in Google Scholar
7. Boyd, J., et al. Advanced Studies in Biometrics. - Lecture Notes in Computer Science. Berlin/Heidelberg, Springer, 2005.
Search in Google Scholar
8. Troje, N. Decomposing Biological Motion: A Framework for Analysis and Synthesis of Human Gait Patterns. - J. of Vision, Vol. 2, 2002, No 5, 371-387.10.1167/2.5.212678652
Search in Google Scholar
9. Venture, G. Human Characterization and Emotion Characterization from Gait.－In: Proc. of the IEEE Int. Conf. Engineering in Medicine and Biology, Buenos Aires, Argentina, 2010, 1292-1295.10.1109/IEMBS.2010.562640421095921
Search in Google Scholar
10. Kobayashi, T., et al. Rotation Invariant Feature Extraction from 3d Acceleration Signals. － In: Proc. of International Conference on Acoustics, Speech, and Signal Processing, 2011, 3684-3687.10.1109/ICASSP.2011.5947150
Search in Google Scholar
11. Vildjiounaite, E., et al. Unobtrusive Multimodal Biometrics for Ensuring Privacy and Information Security with Personal Devices.－Pervasive Computing, 4th International Conference, PERVASIVE, 2006, 187-201.10.1007/11748625_12
Search in Google Scholar
12. Huang, B., et al. Gait Modeling for Human Identification. － In: Proc. of IEEE International Conference on Robotics and Automation, 2007.10.1109/ROBOT.2007.364224
Search in Google Scholar
13. Derawi, M. O., et al. Improved Cycle Detection for Accelerometer Based Gait Authentication. － In: Proc. of Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), Sixth International Conference, 2010.10.1109/IIHMSP.2010.84
Search in Google Scholar
14. Gafurov, D., et al. Gait Recognition Using Wearable Motion Recording Sensors. － EURASIP Journal on Advances in Signal Processing, 2009.10.1155/2009/415817
Search in Google Scholar
15. Kwapisz, J. R., et al. Cell Phonebased Biometric Identification. － In: Proc. of IEEE Fourth International Conference on Biometrics: Theory, Applications and Systems, 2010.
Search in Google Scholar
16. Gafurov, D., et al. Spoof Attacks Ongait. - J. of IEEE Transactions on Information Forensics and Security, Special Issue on Human Detection and Recognition, 2007.
Search in Google Scholar
17. Gafurov, D. Security Analysis of Impostor Attempts with Respect Togender in Gait Biometrics. － In: Proc. of Biometrics: Theory, Applications, and Systems, First IEEE International Conference, 2007.
Search in Google Scholar
18. Gafurov, D., et al. Improved Hip Based Individual Recognition Using Wearable Motion Recording Sensor. － J. of Security Technology, Disaster Recovery and Business Continuity, Volume 122 of Communications in Computer and Information Science, Berlin Heidelberg, Springer, 2010, 179-186.10.1007/978-3-642-17610-4_20
Search in Google Scholar
19. Derawi, M. O., et al. Unobtrusive User-Authentication on Mobile Phones using Biometric Gait Recognition. － In: Proc. Of Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2010 Sixth International Conference, 2010.10.1109/IIHMSP.2010.83
Search in Google Scholar
20. Ailisto, H. J., et al. Identifying People from Gait Pattern with Accelerometers. - J. of Biometric Technology for Human Identification, SPIE, Vol. 5779, 2005.10.1117/12.603331
Search in Google Scholar
21. Mantyjarvi, J., et al. Identifying Users of Portable Devices from Gait Pattern with Accelerometers. - In: Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
Search in Google Scholar
22. Trung, N., et al. Phase Registration in a Gallery Improving Gait Authentication. -Proc. of the International Joint Conference on Biometrics (IJCB2011). IEEE and IAPR’2011.10.1109/IJCB.2011.6117527
Search in Google Scholar
23. Rong, L., et al. Identification of Individual Walking Patterns using Gait Acceleration. - In: Proc. of the 1st International Conference on Bioinformatics and Biomedical Engineering, 2007, 543-546, 10.1109/ICBBE.2007.142
Search in Google Scholar
24. Trung, N., et al. Performance Evaluation of Gait Recognition Using the Largest Inertial Sensor-Based Gait Database. - In: Proc. of the 5th IAPR Int. Conf. on Biometrics, Paper ID 182, New Delhi, India, March 2012, 1-7.10.1109/ICB.2012.6199833
Search in Google Scholar
25. Gelder, B. Towards the Neurobiology of Emotional Body Language. - J. of Nature Reviews, Neuroscience, Vol. 7, March 2006, 242-249.10.1038/nrn187216495945
Search in Google Scholar
26. Taylor, G., et al. Factored Conditional Restricted Boltzmann Machines for Modelling Motion Style.－In: Proc. of the 26th International Conference on Machine Learning, 2009, 1025-1032.10.1145/1553374.1553505
Search in Google Scholar
27. Kadone, H. et al. Symbolic Memory of Motion Patterns by an Associative Memory Dynamics with Self-Organizing Nonmonotonicity. - Neural Information Processing, Vol. 4985, 2008, 203-213.
Search in Google Scholar
28. Zhang, T., et al. Emotion Recognition from Walk Pattern.－In: 21st An. Conf. of the Japanese Neural Network Society, Okinawa, Japan, December 2011, 196-197.
Search in Google Scholar
29. Vapnik, V. The Nature of Statistical Learning Theory. Springer, 1995.10.1007/978-1-4757-2440-0
Search in Google Scholar
30. Burges, C. A Tutorial on Support Vector Machines for Pattern Recognition. － Data Mining and Knowledge Discovery, Vol. 2, 1988, 1-47.
Search in Google Scholar
31. Osuna, E., et al. Support Vector Machines: Training and Applications. － A. I. Memo 1602, MIT Press, 1988.
Search in Google Scholar
32. Karg, M., et al. A Comparison of PCA, KPCA and LDA for Feature Extraction to Recognize Affect in Gait Kinematics.－ In: Proc. of the 3rd International Conference on Affective Computing and Intelligent Interaction, 2009.10.1109/ACII.2009.5349438
Search in Google Scholar

Individual Recognition from Gait Using Feature Value Method

Abstract

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