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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. Presently, 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 article, 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 multipath 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 …