  As an important biomarker for human identification, human gait can be
collected at a distance by passive sensors without subject cooperation, which
plays an essential role in crime prevention, security detection and other human
identification applications. At present, most research works are based on
cameras and computer vision techniques to perform gait recognition. However,
vision-based methods are not reliable when confronting poor illuminations,
leading to degrading performances. In this paper, we propose a novel multimodal
gait recognition method, namely GaitFi, which leverages WiFi signals and videos
for human identification. In GaitFi, Channel State Information (CSI) that
reflects the multi-path propagation of WiFi is collected to capture human
gaits, while videos are captured by cameras. To learn robust gait information,
we propose a Lightweight Residual Convolution Network (LRCN) as the backbone
network, and further propose the two-stream GaitFi by integrating WiFi and
vision features for the gait retrieval task. The GaitFi is trained by the
triplet loss and classification loss on different levels of features. Extensive
experiments are conducted in the real world, which demonstrates that the GaitFi
outperforms state-of-the-art gait recognition methods based on single WiFi or
camera, achieving 94.2% for human identification tasks of 12 subjects.
