Human identification based on natural gait micro-doppler signatures using deep transfer learning

Abstract

Gait-based human identification aims to identify individuals by their walking style. In this study, the authors investigate the use of micro-Doppler (m-D) signatures retrieved from a frequency-modulated continuous-wave radar sensor to identify individuals based on their natural gait characteristics. The gait dataset of 20 persons has been collected in an indoor environment where each subject was allowed to walk naturally and freely, which is absolutely more realistic and challenging than most existing works based on limited walking behaviour. Then, they perform identification using a transfer learned ResNet-50, which was fine-tuned on the gait m-D dataset based on the deep transfer learning technique. Through experiments, they first determined the optimal observation window length of m-D samples, and with this input, they achieved an average identification accuracy of 96.7% on the test set for 20 subjects, which highly outperforms the state-of-the-art methods. The presented work provides prospects in developing a solution to automatically identify persons based on gait characteristics using a simple and cost-efficient radar device.