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Human identification via unsupervised feature learning from UWB radar data

Citation

Yin, J and Tran, SN and Zhang, Q, Human identification via unsupervised feature learning from UWB radar data, Lecture Notes in Computer Science, volume 10937 - Proceedings of the 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018): Advances in Knowledge Discovery and Data Mining, 3-6 June 2018, Melbourne, Australia ISBN 978-3-319-93033-6 (2018) [Refereed Conference Paper]


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Copyright 2018 Springer

DOI: doi:10.1007/978-3-319-93034-3_26

Abstract

This paper presents an automated approach to automatically distinguish the identity of multiple residents in smart homes. Without using any intrusive video surveillance devices or wearable tags, we achieve the goal of human identification through properly processing and analyzing the received signals from the ultra-wideband (UWB) radar installed in indoor environments. Because the UWB signals are very noisy and unstable, we employ unsupervised feature learning techniques to automatically learn local, discriminative features that can incorporate intra-class variations of the same identity, and yet reflect differences in distinguishing different human identities. The learned features are then used to train an SVM classifier and recognize the identity of residents. We validate our proposed solution via extensive experiments using real data collected in real-life situations. Our findings show that feature learning based on K-means clustering, coupled with whitening and pooling, achieves the highest accuracy, when only limited training data is available. This shows that the proposed feature learning and classification framework combined with the UWB radar technology provides an effective solution to human identification in multi-residential smart homes.

Item Details

Item Type:Refereed Conference Paper
Keywords:human identification, unsupervised feature learning, UWB, smart home
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Health
Objective Group:Specific population health (excl. Indigenous health)
Objective Field:Health related to ageing
UTAS Author:Tran, SN (Dr Son Tran)
ID Code:140698
Year Published:2018
Web of Science® Times Cited:5
Deposited By:Information and Communication Technology
Deposited On:2020-09-01
Last Modified:2020-12-08
Downloads:2 View Download Statistics

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