Spatial footstep recognition by convolutional neural networks for biometrie applications
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Institute of Electrical and Electronics Engineers IncDate
2016Citation
10.1109/ICSENS.2016.7808890
IEEE Sensors Conference, SENSORS 2016. IEEE, 2016. 1-3
ISSN
1930-0395ISBN
978-147998287-5DOI
10.1109/ICSENS.2016.7808890Subjects
Pattern recognition; Machine learning; Convolutional neural networks; Gait analysis; Floor sensor system; TelecomunicacionesRights
© 2016 IEEEAbstract
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
Files in this item
Google Scholar:Costilla-Reyes, Omar
-
Vera Rodríguez, Rubén
-
Scull, Patricia
-
Ozanyan, Krikor B.
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