Reliable gait recognition under low-light conditions remains challenging for traditional cameras. Event cameras, with their high dynamic range and fine temporal resolution, offer a promising alternative but suffer from sparse signals under small motion amplitudes and vulnerability to noise or irrelevant movements (e.g., shadows). To address these issues, we propose FusionGait, a complementary fusion framework that integrates event with standard frames to achieve robust gait recognition. Specifically, we propose a Self-supervised Hierarchical Feature Extractor (SSL-HFE) built upon DINOv2, which employs learnable prompts to bridge the gap between gray frames and RGB frames, extracts multi-level semantic features, and enhances their discriminability through a self-supervised learning strategy. Then, we introduce the Complementary Fusion Learning Module (CFLM), which employs cross-cost volumes to explicitly model pixel-level correlations between frames and events, enabling effective cross-modal interaction and fusion. Furthermore, we propose EvGSimulator, a sensor-specific data augmentation strategy that simulates diverse illumination conditions based on physical properties. The framework maintains robustness even when real frames are unavailable by reconstructing frame-like representations from events, and it scales to ultra–high-frame-rate scenarios with hundreds of frames per second. We also collect DAVIS346-Gait-RGE, the multi-view semi-indoor & outdoor gait dataset captured with a DAVIS346 event camera, including three modalities: event streams, gray frames, and reconstructed frames. Experiments across multiple datasets show FusionGait achieves state-of-the-art performance, effectively surpassing single-modality methods.

Current gait recognition methods heavily rely on various gait representations (e.g., silhouette sequences) generated by task-specific, supervised upstream processes, which inevitably incur high annotation costs and the risk of cumulative errors. Recently, generic knowledge from task–agnostic large visual models (LVMs) has been successfully applied to gait recognition, freeing the field from such dependencies. However, this approach does not address challenges posed by traditional cameras in handling scenarios with low latency, high speed, and high dynamic range. In this paper, we introduce EdinoGait, a novel and effective gait recognition framework that leverages event-based LVMs to overcome the scarcity of large-scale event-based datasets. Specifically, due to the distinct modality gap between image and event data and the lack of large-scale datasets, transferring LVMs to event-based vision is non-trivial. To address this, we introduce a novel event encoder that mitigates the modality gap through event prompts and a$CLS$patch contrastive loss. Subsequently, we design an autoencoder-based dual-alignment module to eliminate background noise brought by LVMs while preserving the motion details provided by event data. Additionally, to promote the application of event cameras in gait recognition, we collect the first semi-indoor, multi-view gait dataset captured by the DAVIS346 event camera. This dataset comprises 6,150 sequences (two modalities: grayscale images and event streams) of 41 subjects captured under two lighting conditions and five view angles ($0^{\circ }$,$45^{\circ }$,$90^{\circ }$,$135^{\circ }$, and$180^{\circ }$). Specifically, for each lighting condition and viewing angle, there are six sequences representing normal walking (NM), three representing walking with a backpack (BG), three with a portable bag (PT), and three with a coat (CL). Comprehensive experiments conducted on our event-based gait dataset and EV-CASIA-B demonstrate that EdinoGait significantly outperforms frame-based LVMs. Notably, under low-light conditions, the recognition accuracy of frame-based LVMs declines sharply, while EdinoGait exhibits robust performance.

Traditional cameras struggle to perform in challenging scenarios such as low latency, high speed and high dynamic range. In contrast, neuromorphic vision sensors (event cameras) have great potential for robotics and computer vision due to the advantages of high temporal resolution, high dynamic range, and ultra-low resource consumption. Event cameras are novel bio-inspired sensors that monitor the brightness change of each pixel asynchronously and provide a stream of events encoding the time, position and sign of the brightness changes. Hence, traditional computer vision methods cannot be directly applied to the event-stream. Finding event representations that completely maintain event attributes, as well as efficient and accurate learning approaches, is the key to unlocking the potential of event cameras. In this study, we reveal the rigid transfer from event-stream to graph that has been overlooked in previous work and introduce a novel event representation, namely event graph sequence (EGS) considering the local and global temporal clues. Coupled with EGS, we propose a spatio-temporal pattern extracting (STPE) module to capture the spatio-temporal correlation and evolution of EGS. Our novel framework, Event Graph Sequence Transformer (EGST), exploits event properties to provide efficient and accurate recognition. This study focuses on the event-based human gaits recognition task, and EGST is evaluated on three different event-based gait datasets. The evaluation results show better or comparable accuracy than the state-of-the-art, while requiring extremely low computation resources. The code will be available athttps://github.com/C19h/EGST.