TY - GEN
T1 - Spatial footstep recognition by convolutional neural networks for biometrie applications
AU - Costilla-Reyes, Omar
AU - Vera-Rodriguez, Ruben
AU - Scully, Patricia
AU - Ozanyan, Krikor B.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/1/5
Y1 - 2016/1/5
N2 - 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 (EER) 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.
AB - 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 (EER) 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.
KW - convolutional neural networks
KW - deep learning
KW - floor sensor system
KW - gait analysis
KW - machine learning
KW - pattern recognition
UR - https://www.scopus.com/pages/publications/85011011255
U2 - 10.1109/ICSENS.2016.7808890
DO - 10.1109/ICSENS.2016.7808890
M3 - Conference Publication
AN - SCOPUS:85011011255
T3 - Proceedings of IEEE Sensors
BT - IEEE Sensors, SENSORS 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE Sensors Conference, SENSORS 2016
Y2 - 30 October 2016 through 2 November 2016
ER -