Methods for person identification on a pressure-sensitive floor: Experiments with multiple classifiers and reject option

Information Fusion

Volumn 9, Issue 1, 2008, Pages 21-40

  Suutala, Jaakko ^a   Röning, Juha ^a  

^a UNIVERSITY OF OULU   (Finland)

Author keywords

Biometric identification; Ensemble of classifiers; Intelligent environment; Pattern recognition; Pressure sensitive floor; Reject option

Indexed keywords

OBJECT RECOGNITION; PROBLEM SOLVING; SENSITIVITY ANALYSIS; SENSORY PERCEPTION;

BIOMETRIC IDENTIFICATIONS; ENSEMBLE OF CLASSIFIERS; PRESSURE SENSITIVE FLOORS; SENSORY SYSTEMS;

BIOMETRICS;

EID: 35348924230     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2006.11.003     Document Type: Article

References (55)

1. I.A. Essa, Ubiquitous sensing for smart and aware environments: technologies towards the building of an aware home, IEEE Personal Communications, October 2000, Special issue on networking the physical world, 2000, pp. 47-49.
2. Brummit B., Meyers B., Krumm J., Kern A., and Shafer S. Easyliving: technologies for intelligent environments. 2nd International Symposium of Handheld and Ubiquitous Computing (HUC) (2000), Springer-Verlag, New York, USA 12-29
3. Pentland A. Smart rooms. Scientific American 274 (1996) 68-76
4. Paajanen M., Lekkala J., and Kirjavainen K. Electromechanical film (EMFi) - a new multipurpose electret material. Sensors and Actuators A 84 (2000) 95-102
5. Weiser M., and Brown J. The coming age of calm technology. Beyond Calculation: The Next Fifty years of Computing (1997), Springer-Verlag, New York
6. P. Cattin, Biometric authentication system using human gait, Ph.D. Thesis, ETH-Zürich, Institute of Robotics, Switzerland, 2002.
7. Jain A., Hong L., and Pankanti S. Biometric identification. Communications of the ACM 43 2 (2000) 91-98
8. Little J., and Boyd J. Recognizing people by their gait: the shape of motion. MIT Press Journal Videre 1 (1998) 1-32
9. Huang P., Harris C., and Nixon M. Comparing different template features for recognizing people by their gait. In: Carter J., and Nixon M. (Eds). Proceedings of Ninth British Machine Vision Conference (BMVC) (1998), British Machine Vision Association, Southamton, UK 639-648
10. A. Kale, A. Rajagopalan, N. Cuntoor, V. Krüger, Gait based recognition of humans using continuous HMMs, in: Proceedings of the International Conference on Face and Gesture Recognition, Washington, DC, USA, 2002, pp. 321-326.
11. Orr R., and Abowd G. The smart floor: a mechanism for natural user identification and tracking. Proceedings of 2000 Conf. Human Factors in Computing Systems (CHI) (2000), ACM Press, The Hague, Netherlands 275-276
12. Addlesee M., Jones A., Livesey F., and Samaria F. ORL active floor. IEEE Personal Communications 4 5 (1997) 35-41
13. J.-S. Yun, S.-H. Lee, W.-T. Woo, J.-H. Ryu, The user identification system using walking pattern over the ubifloor, in: Proceedings of International Conference on Control, Automation, and Systems (ICCAS), Gyeongju, Korea, 2003.
14. T. Murakita, T. Ikeda, H. Ishiguro, Human tracking using floor sensors based on the markov chain Monte Carlo method, in: Proceedings of Seventeenth International Conference on Pattern Recognition (ICPR), Cambridge, UK, 2004, pp. 917-920.
15. H. Morishita, R. Fukui, T. Sato, High resolution pressure sensor distributed floor for future human-robot symbiosis environment, in: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, EPFL, Lausanne, Switzerland, 2002, pp. 1246-1251.
16. K. Sudo, J. Yamato, A. Tomono, Determining gender of walking people using multiple sensors, in: Proceedings of the IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, 1996, pp. 641-646.
17. Eng K., Douglas R., and Verschure P. An interactive space that learns to influence of human behaviour. IEEE Transaction on Systems, Man, and Cybernetics-Part A: Systems and Humans 35 1 (2005) 66-77
18. R. Headon, R. Curwen, Recognizing movements from the ground reaction force, in: Proceedings of the Workshop on Perceptive User Interfaces, Orlando, FL, USA, 2001, pp. 1-8.
19. M. Köhle, D. Merk, Clinical gait analysis by neural networks: issues and experiences, in: Proceedings of the IEEE Symposium on Computer-Based Medical Systems, Maribor, Slovenia, 1997, pp. 138-143.
20. Duin R., and Tax D. Experiments with classifier combining rules. In: Kittler J., and Roli F. (Eds). Proc. of First International Workshop on Multiple Classifier Systems (MCS). Lecture Notes in Computer Science vol. 1857 (2000), Springer-Verlag, Berlin, Cagliari, Italy 16-29
21. Kittler J., Hatef M., Duin R.P.W., and Matas J. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 3 (1998) 226-239
22. Ho T.K., Hull J., and Srihari S.N. Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 16 1 (1994) 66-75
23. Ross A., and Jain A.K. Information fusion in biometrics. Pattern Recognition Letters 24 13 (2003) 2115-2125
24. Jain A.K., Prabhakar S., and Chen S. Combining multiple matchers for a high security finger-print verification system. Pattern Recognition Letters 30 11-13 (1999) 1371-1379
25. Kittler J., Matas J., Jonsson K., and Sanchez M.R. Combining evidence in personal identity verification systems. Pattern Recognition Letters 18 (1997) 845-852
26. M. Cheung, M. Mak, S. Kung, Multi-sample data-dependent fusion of sorted score sequences for biometric authentication, in: Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Montreal, Canada, 2004, pp. 681-684.
27. N. Poh, S. Bengio, J. Korczak, A multi-sample multi-source model for biometric authentication, in: Proc. of IEEE 12th Workshop on Neural Networks for Signal Processing (NNSP), 2002, pp. 375-384.
28. K. Koho, J. Suutala, T. Seppänen, J. Röning, Footstep pattern matching from pressure signals using semi-Markov models, in: Proceedings of 12th European Signal Processing Conference (EUSIPCO), Vienna, Austria, 2004, pp. 1609-1612.
29. X. Ge, P. Smyth, Deformable markov model templates for time-series pattern matching, in: Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, 2000, pp. 81-90.
30. Pirttikangas S., Suutala J., Riekki J., and Röning J. Learning vector quantization in footstep identification. In: Hamza M. (Ed). Proceedings of 3rd IASTED International Conference on Artificial Intelligence and Applications (AIA), IASTED (2003), ACTA Press, Benalmadena, Spain 413-417
31. J. Suutala, J. Röning, Towards the adaptive identification of walkers: automated feature selection of footsteps using distinction-sensitive LVQ, in: Proceedings of International Workshop on Processing Sensory Information for Proactive Systems (PSIPS), Oulu, Finland, 2004, pp. 61-67.
32. J. Suutala, J. Röning, Combining classifiers with different footstep feature sets and multiple samples for person identification, in: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 5, Philadelphia, PA, USA, 2005, pp. 357-360.
33. Bishop C.M. Neural Networks for Pattern Recognition (1995), Oxford University Press Inc., New York
34. S. Pirttikangas, J. Suutala, J. Riekki, J. Röning, Footstep identification from pressure signals using Hidden Markov Models, in: H. Huttunen, A. Gotchev, A. Vasilache (Eds.), Proceedings of Finnish Signal Processing Symposium (FINSIG), Tampere, Finland, 2003, pp. 124-128.
35. Mitchell T.M. Machine Learning. Computer Science Series (1997), McGraw-Hill International Editions
36. Kohonen T. Self-organizing Maps. 3rd ed. (2001), Springer-Verlag, Berlin, Heidelberg, New York
37. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (2000), Cambridge University Press, UK
38. Vapnik V. Statistical Learning Theory (1998), John Wiley, New York
39. Duin R., and Tax D. Classifier conditional posterior probabilities. In: Amin A., Dori D., Pudil P., and Freeman H. (Eds). Advances in Pattern Recognition, Proc. of Joint IAPR Int. Workshops SSPR and SPR. Lecture Notes in Computer Science vol. 1451 (1998), Springer-Verlag, Sydney, Australia 611-619
40. I.T. Nabney, Efficient training of RBF networks for classification, in: Proceedings of International Conference on Artificial Neural Networks (ICANN), Edinburgh, UK, 1999, pp. 210-215.
41. Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola A., Bartlett P., Schölkopf B., and Shuurmans D. (Eds). Advances in Large Margin Classifiers (1999), MIT Press, Cambridge, MA, USA 61-74
42. B. Zadrozny, C. Elkan, Transforming classifier scores into accurate multiclass probability estimates, in: Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining (KDD), Edmonton, Canada, 2002, pp. 694-699.
43. C.-C. Chang, C.-J. Lin, LIBSVM: A Library for Support Vector Machines, 2001. Available from: .
44. H.-T. Lin, C.-J. Lin, R.C. Weng, A note on Platt's probabilistic outputs for support vector machines, Tech. rep., National Taiwan University, 2003.
45. Wu T.-F., Lin C.-J., and Weng R.C. Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research 5 (2004) 975-1005
46. Hastie T., and Tibshirani R. Classification by pairwise coupling. In: Jordan M.I., and Kearns M.J. (Eds). Advances in Neural Information Processing Systems 10 (NIPS10) (1998), MIT Press 507-513
47. Chow C.K. On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory 16 (1970) 41-46
48. De Stefano C., Sansone C., and Vento M. To reject or not to reject: that is the question - an answer in case of neural classifiers. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews 30 1 (2000) 84-94
49. A. Martin, M. Przybocki, NIST 2003 language recognition evaluation, in: In EUROSPEECH-2003, Geneva, Switzerland, 2003, pp. 1341-1344.
50. Suutala J., Pirttikangas S., Riekki J., and Röning J. Reject-optional LVQ-based two-level classifier to improve reliability in footstep identification. In: Ferscha A., and Mattern F. (Eds). Proceedings of 2nd International Conference on Pervasive Computing (PERVASIVE). Lecture Notes in Computer Science vol. 3001 (2004), Springer-Verlag, Linz/Vienna, Austria 182-187
51. Foggia P., Sansone C., Tortorella F., and Vento M. Multiclassification: reject criteria for the Bayesian combiner. Pattern Recognition 32 (1999) 1435-1447
52. Shawe-Taylor J., and Cristianini N. Kernel Methods for Pattern Analysis (2004), Cambridge University Press, UK
53. Nabney I. Netlab: Algorithms for Pattern Recognition (2004), Springer-Verlag. http://www.ncrg.aston.ac.uk/netlab/ Available from:
54. T. Kohonen, J. Hynninen, J. Kangas, J. Laaksonen, K. Torkkola, LVQ_PAK: The learning vector quantization program package, Tech. rep., Helsinki University of Technology, Finland. Available from: , 1996.
55. Tax D., van Breukelen M., Duin R., and Kittler J. Combining classifiers by averaging or by multiplying?. Pattern Recognition 33 (2000) 1475-1485