Robust Gait Recognition Based on Deep CNNs With Camera and Radar Sensor Fusion

Abstract

In recent years, gait recognition has emerged as an important and promising solution for human identification. Generally, gait recognition is based on a single type of sensor, such as a camera or a radar. However, data of a single modality may only capture inadequate gait features of a person, such as camera data lacking the intuitive micro-motion pattern information and radar data lacking the information about gait appearance, making gait-based human identification system vulnerable to complex covariate conditions, e.g. cross-view and cross-walking-condition. To build a robust and reliable gait-based human identification system, in this study, we propose a multi-sensor gait recognition framework with deep convolutional neural networks (CNNs) by fusing camera gait energy images (GEIs) and radar time-Doppler spectrograms. To learn the fine-grained gait appearance features, we propose a body-part spatial attention (BPSA) module to obtain more discriminative body part representations of GEIs. To learn the gait micro-motion pattern, we propose a long-short temporal relation modeling (LSTRM) module to obtain the local and global micro-motion representation of time-Doppler spectrograms. Finally, we fuse the discriminative body part representation and the micro-motion pattern at the multi-scale feature space to obtain richer and more robust gait features for human identification. We provide an extensive empirical evaluation in terms of various complex covariate conditions, namely, cross-view and cross-walking-condition. Experiments on 121 subjects with eight views and three walking conditions of camera and radar data show our proposed method is more robust and accurate.