LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition
This work addresses gait recognition for biometric identification, presenting an incremental improvement through novel fusion techniques.
The paper tackles gait recognition by proposing LiCAF, a LiDAR-camera fusion method that uses asymmetric modeling to address issues like overlooked modality characteristics and lack of fine-grained fusion, achieving state-of-the-art performance with 93.9% Rank-1 and 98.8% Rank-5 accuracy on the SUSTech1K dataset.
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.