The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - April (2013 vol.35)
pp: 823-834
R. Vera-Rodriguez , Biometric Recognition Group-ATVS, Univ. Autonoma de Madrid, Madrid, Spain
J. S. D. Mason , Speech & Image Res. Group, Swansea Univ., Swansea, UK
J. Fierrez , Biometric Recognition Group-ATVS, Univ. Autonoma de Madrid, Madrid, Spain
J. Ortega-Garcia , Biometric Recognition Group-ATVS, Univ. Autonoma de Madrid, Madrid, Spain
ABSTRACT
Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatiotemporal information contained in the footstep signals for person recognition. Experiments are carried out on the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 people. Results show very similar performance for both spatial and temporal approaches (5 to 15 percent EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5 to 10 percent EER). The assessment protocol is focused on the influence of the quantity of data used in the reference models, which serves to simulate conditions of different potential applications such as smart homes or security access scenarios.
INDEX TERMS
Databases, Feature extraction, Legged locomotion, Sensor fusion, Sensor phenomena and characterization, Intelligent sensors,pattern recognition, Biometrics, footstep recognition, gait recognition, pressure analysis
CITATION
R. Vera-Rodriguez, J. S. D. Mason, J. Fierrez, J. Ortega-Garcia, "Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 4, pp. 823-834, April 2013, doi:10.1109/TPAMI.2012.164
REFERENCES
[1] A. Itai and H. Yasukawa, "Footstep Classification Using Simple Speech Recognition Technique," Proc. IEEE Int'l Symp. Circuits and Systems, pp. 3234-3237, 2008.
[2] L. Middleton, A.A. Buss, A.I. Bazin, and M.S. Nixon, "A Floor Sensor System for Gait Recognition," Proc. IEEE Workshop Automatic Identification Advanced Technologies, pp. 171-176, 2005.
[3] G. Qian, J. Zhang, and A. Kidane, "People Identification Using Floor Pressure Sensing and Analysis," IEEE Sensors J., vol. 10, no. 9, pp. 1447-1460, Sept. 2010.
[4] J. Yun, "User Identification Using Gait Patterns on UbiFloorii," Sensors, vol. 11, no. 3, pp. 2611-2639, 2011.
[5] M.S. Nixon, T.N. Tan, and R. Chellappa, Human Identification Based on Gait. Springer, 2005.
[6] R. Vera-Rodriguez, P. Tome, J. Fierrez, and J. Ortega-Garcia, "Fusion of Footsteps and Face Biometrics on an Unsupervised and Uncontrolled Environment," Proc. SPIE Biometric Technology for Human Identification IX, 2012.
[7] R. Vera-Rodriguez, N.W.D. Evans, and J.S.D. Mason, "Footstep Recognition," Encyclopedia of Biometrics, pp. 550-557, Springer, 2009.
[8] A. Pedotti, "Simple Equipment Used in Clinical Practice for Evaluation of Locomotion," IEEE Trans. Biomedical Eng., vol. 24, no. 5, pp. 456-461, Sept. 1977.
[9] M.D. Addlesee, A. Jones, F. Livesey, and F. Samaria, "The ORL Active Floor," IEEE Personal Comm., vol. 4, pp. 35-41, Oct. 1997.
[10] J. Suutala and J. Roning, "Methods for Person Identification on a Pressure-Sensitive Floor: Experiments with Multiple Classifiers and Reject Option," Information Fusion, vol. 9, pp. 21-40, 2008.
[11] R. Vera-Rodriguez, R.P. Lewis, J.S.D. Mason, and N.W.D. Evans, "Footstep Recognition for a Smart Home Environment," Int'l J. Smart Home, vol. 2, pp. 95-110, 2008.
[12] R.J. Orr and G.D. Abowd, "The Smart Floor: A Mechanism for Natural User Identification and Tracking," Proc. Conf. Human Factors in Computing Systems, pp. 1-9, 2000.
[13] C. Cattin, "Biometric Authentication System Using Human Gait," PhD dissertation, ETH Zurich, 2002.
[14] J.S. Yun, S.H. Lee, W.T. Woo, and J.H. Ryu, "The User Identification System Using Walking Pattern over the ubiFloor," Proc. Int'l Conf. Control, Automation, and Systems, pp. 1046-1050, 2003.
[15] J.-W. Jung, T. Sato, and Z. Bien, "Dynamic Footprint-Based Person Recognition Method Using a Hidden Markov Model and a Neural Network: Research Articles," Int'l J. Intelligent Systems, vol. 19, pp. 1127-1141, Nov. 2004.
[16] J. Suutala and J. Roning, "Combining Classifiers with Different Footstep Feature Sets and Multiple Samples for Person Identification," Proc. Int'l Conf. Acoustics, Speech, and Signal Processing, vol. 5, pp. 357-360, 2005.
[17] Y. Gao, M.J. Brennan, B.R. Mace, and J.M. Muggleton, "Person Recognition by Measuring the Ground Reaction Force Due to a Footstep," Proc. Ninth Int'l Conf. Recent Advances in Structural Dynamics, 2006.
[18] J.P. Stevenson, S.L. Firebaugh, and H.K. Charles, "Biometric Identification from a Floor Based PVDF Sensor Array Using Hidden Markov Models," Proc. Sensors Applications Symp. Technology Conf., 2007.
[19] J. Suutala, K. Fujinami, and J. Röning, "Gaussian Process Person Identifier Based on Simple Floor Sensors," Proc. European Conf. Smart Sensing and Context, pp. 55-68, 2008.
[20] R. Vera-Rodriguez, J.S.D. Mason, and N.W.D. Evans, "Assessment of a Footstep Biometric Verification System," Advances in Pattern Recognition: Handbook of Remote Biometrics, pp. 313-327, Springer, 2009.
[21] R. Vera-Rodriguez, J.S.D. Mason, J. Fierrez, and J. Ortega-Garcia, "Analysis of Time Domain Information for Footstep Recognition," Proc. Int'l Symp. Visual Computing, pp. 489-498, 2010.
[22] R. Vera-Rodriguez, J. Mason, J. Fierrez, and J. Ortega-Garcia, "Analysis of Spatial Domain Information for Footstep Recognition," IET Computer Vision, vol. 5, no. 6, pp. 380-388, Nov. 2011.
[23] A.K. Jain, A. Ross, and S. Prabhakar, "An Introduction to Biometric Recognition," IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4-20, Jan. 2004.
[24] J. Suutala, S. Pirttikangas, J. Riekki, and J. Roning, "Reject-Optional LVQ-Based Two-Level Classifier to Improve Reliability in Footstep Identification," Proc. Second Int'l Conf. Pervasive Computing, vol. 3001, pp. 182-187, 2004.
[25] R. Vera-Rodriguez, J.S.D. Mason, and N.W.D. Evans, "Automatic Cross-Biometric Footstep Database Labelling Using Speaker Recognition," Proc. Int'l Conf. Biometrics, pp. 503-512, 2009.
[26] "The Swansea Footstep Biometric Database (SFootBD)," http://atvs.ii.uam.esdatabases.jsp, 2012.
[27] V.N. Vapnik, Statistical Learning Theory. Wiley, 1998.
[28] NIST, "Speaker Recognition Evaluation Campaign," http://www.itl.nist.gov/iad/mig/testssre /, 2010.
[29] A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki, "The DET Curve in Assessment of Detection Task Performance," Proc. Eurospeech, vol. 1, pp. 1895-1898, 1997.
[30] N. Poh, "Multi-System Biometric Authentication: Optimal Fusion and User-Specific Information," PhD dissertation, Ecole Polytechnique Federale de Lausanne, 2006.
[31] A. Jain, N. Karthik, and A. Ross, "Score Normalization in Multimodal Biometric Systems," Pattern Recognition, vol. 38, no. 12, pp. 2270-2285, 2005.
[32] J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, "On Combining Classifiers," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239, Mar. 1998.
[33] J. Fierrez-Aguilar, "Adapted Fusion Schemes for Multimodal Biometric Authentication," PhD dissertation, Universidad Politecnica de Madrid, May 2006.
197 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool