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Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition
April 2013 (vol. 35 no. 4)
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
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:
spatiotemporal phenomena,biometrics (access control),image fusion,image recognition,person recognition,comparative analysis,spatiotemporal information fusion,footstep recognition,biometric,walking characteristics,floor-based sensors,footstep signals,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 and Machine Intelligence, vol. 35, no. 4, pp. 823-834, April 2013, doi:10.1109/TPAMI.2012.164
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