@inbook{c4b3bdfe22f44909af7d602589d25572,
title = "GMM-UBM based person verification using footfall signatures for smart home applications",
abstract = "In this paper, we propose a novel person verification system based on footfall signatures using Gaussian Mixture Model-Universal Background Model (GMM-UBM). Ground vibration generated by footfall of an individual is used as a biometric modality. We conduct extensive experiments to compare the proposed technique with various baselines of footfall based person verification. The system is evaluated on an indigenous dataset containing 7750 footfall events of twenty subjects. Different scenarios are created for analyzing the robustness of the system by varying the number of registered and non registered users. We obtained a Half Total Error Rate (HTER) of 7\% with the proposed model and achieved an overall performance gain of \textasciitilde{}46\% and \textasciitilde{}33\% over Support Vector Machine (SVM) and Convolution Neural Network (CNN) based techniques respectively. Experimental results validate the efficacy of the proposed algorithms.",
keywords = "GMM-UBM, Novelty detection, Person verification, Seismic sensor, Smart homes",
author = "Sahil Anchal and Bodhibrata Mukhopadhyay and Manohar Parvatini and Subrat Kar",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 ; Conference date: 11-11-2019 Through 14-11-2019",
year = "2019",
month = nov,
doi = "10.1109/GlobalSIP45357.2019.8969215",
language = "English",
series = "GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings",
address = "United States",
}