Spatial footstep recognition by convolutional neural networks for biometrie applications
EntityUAM. Departamento de Tecnología Electrónica y de las Comunicaciones
PublisherInstitute of Electrical and Electronics Engineers Inc
10.1109/ICSENS.2016.7808890IEEE Sensors Conference, SENSORS 2016. IEEE, 2016. 1-3
SubjectsPattern recognition; Machine learning; Convolutional neural networks; Gait analysis; Floor sensor system; Telecomunicaciones
Rights© 2016 IEEE
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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