TY  - EJOU
AU  - Luo, Zhongbin 
AU  - Guan, Zhaoyang 
AU  - You, Wenxing 
AU  - Wang, Yunteng 
AU  - Bi, Yanqiu 

TI  - GaitMAFF: Adaptive Multi-Modal Fusion of Skeleton Maps and Silhouettes for Robust Gait Recognition in Complex Scenarios
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 87
IS  - 2
SN  - 1546-2226

AB  - Gait recognition is a key biometric for long-distance identification, yet its performance is severely degraded by real-world challenges such as varying clothing, carrying conditions, and changing viewpoints. While combining silhouette and skeleton data is a promising direction, effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem. This paper introduces GaitMAFF, a novel Multi-modal Adaptive Feature Fusion Network, to address this challenge. Our approach first transforms discrete skeleton joints into a dense Skeleton Map representation to align with silhouettes, then employs an attention-based module to dynamically learn the fusion weights between the two modalities. These fused features are processed by a powerful spatio-temporal backbone with Weighted Global-Local Feature Fusion Modules (WFFM) to learn a discriminative representation. Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance, with an average Rank-1 accuracy of 84.6% on CCPG and 58.7% on Gait3D. These results demonstrate that our adaptive fusion strategy effectively integrates complementary multi-modal information, significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.
KW  - Gait recognition; multi-modal fusion; adaptive feature fusion; skeleton map; silhouette

DO  - 10.32604/cmc.2025.075704