Human Identification via Unsupervised Feature Learning from UWB Radar Data

Abstract

This paper presents an automated approach to automatically distinguishing 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 identity. The learned features are then used to train an SVM classifier and recognize the identity of residents. We demonstrate the effectiveness of our proposed solution through 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 a limited quantity of 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.

Method based on UWB for user identification during gait periods

“…In particular, the performance of printed antennas based on conductive polymers remains good also when crumpled because of clothes bending. UWB was also used for identification purposes, but according to a non-wearable approach [15]: a transmitter and a receiver were mounted on the top of a door frame; the UWB signals, scattered by the body of people passing through the doorway, were used to predict the identity of the user by means of unsupervised feature learning and classification; accuracy was ∼80% with eight users. A similar non-wearable approach was proposed for human fall detection in [16], showing promising outcomes.…”