Human Gait Recognition Based on Frontal-View Walking Sequences Using Multi-modal Feature Representations and Learning

Despite that much progress has been reported in gait recognition, most of these existing works adopt lateral-view parameters as gait features, which requires large area of data collection environment and limits the applications of gait recognition in real-world practice. In this paper, we adopt frontal-view walking sequences rather than lateral-view sequences and propose a new gait recognition method based on multi-modal feature representations and learning. Specifically, we characterize walking sequences with two different kinds of frontal-view gait features representations, including holistic silhouette and dense optical flow. Pedestrian regions extraction is achieved by an improved YOLOv7 algorithm called Gait-YOLO algorithm to eliminate the effects of background interference. Multi-modal fusion module (MFM) is proposed to explore the intrinsic connections between silhouette and dense optical flow features by using squeeze and excitation operations at the channel and spatial levels. Gait feature encoder is further used to extract global walking characteristics, enabling efficient multi-modal information fusion. To validate the efficacy of the proposed method, we conduct experiments on CASIA-B and OUMVLP gait databases and compare performance of our proposed method with other existing state-of-the-art gait recognition methods.