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Spatial footstep recognition by convolutional neural networks for biometrie applications

Author
Costilla-Reyes, Omar; Vera-Rodriguez, Rubén; Scull, Patricia; Ozanyan, Krikor B.
Entity
UAM. Departamento de Tecnología Electrónica y de las Comunicaciones
Publisher
Institute of Electrical and Electronics Engineers Inc
Date
2016
Citation
10.1109/ICSENS.2016.7808890
IEEE Sensors Conference, SENSORS 2016. IEEE, 2016. 1-3
 
 
 
ISSN
1930-0395
ISBN
978-147998287-5
DOI
10.1109/ICSENS.2016.7808890
Subjects
Pattern recognition; Machine learning; Convolutional neural networks; Gait analysis; Floor sensor system; Telecomunicaciones
URI
http://hdl.handle.net/10486/678841
Rights
© 2016 IEEE

Abstract

We propose a Convolutional Neural Network model to learn spatial footstep features end-to-end from a floor sensor system for biometric applications. Our model’s generalization performance is assessed by independent validation and evaluation datasets from the largest footstep database to date, containing nearly 20,000 footstep signals from 127 users. We report footstep recognition performance as Equal Error Rate in the range of 9% to 13% depending on the test set. This improves previously reported footstep recognition rates in the spatial domain up to 4% EER
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Google™ Scholar:Costilla-Reyes, Omar - Vera-Rodriguez, Rubén - Scull, Patricia - Ozanyan, Krikor B.

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  • Producción científica en acceso abierto de la UAM [14677]

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