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Comparative Analysis and Fusion of Spatiotemporal Information for Footstep Recognition

Ruben Vera-Rodriguez, John Mason, Julian Fierrez, Javier Ortega-Garcia

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 35, Issue: 4, Pages: 823 - 834

Swansea University Author: John Mason

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DOI (Published version): 10.1109/tpami.2012.164

Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN: 0162-8828 2160-9292
Published: 2013
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Item Description: Footstep recognition is a relatively new biometric, which aims to discriminate persons using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatio-temporal 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 persons. Results show very similar performance for both spatial and temporal approaches (5% to 15% EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5% to 10% 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.
College: Faculty of Science and Engineering
Issue: 4
Start Page: 823
End Page: 834