  Gait recognition is a biometric technology that identifies individuals by
using walking patterns. Due to the significant achievements of multimodal
fusion in gait recognition, we consider employing LiDAR-camera fusion to obtain
robust gait representations. However, existing methods often overlook intrinsic
characteristics of modalities, and lack fine-grained fusion and temporal
modeling. In this paper, we introduce a novel modality-sensitive network LiCAF
for LiDAR-camera fusion, which employs an asymmetric modeling strategy.
Specifically, we propose Asymmetric Cross-modal Channel Attention (ACCA) and
Interlaced Cross-modal Temporal Modeling (ICTM) for cross-modal valuable
channel information selection and powerful temporal modeling. Our method
achieves state-of-the-art performance (93.9% in Rank-1 and 98.8% in Rank-5) on
the SUSTech1K dataset, demonstrating its effectiveness.
