LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition

TL;DR

This paper introduces LiCAF, a novel LiDAR-camera fusion network for gait recognition that employs asymmetric modeling strategies to improve robustness and accuracy, achieving state-of-the-art results on the SUSTech1K dataset.

Contribution

LiCAF is the first to use asymmetric cross-modal channel attention and interlaced temporal modeling for LiDAR-camera gait recognition.

Findings

Achieves 93.9% Rank-1 accuracy on SUSTech1K dataset.

Outperforms existing methods in gait recognition accuracy.

Demonstrates effective cross-modal feature fusion and temporal modeling.

Abstract

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.