@article{Luo2026, 
author = {Zhongbin Luo and Zhaoyang Guan and Wenxing You and Yunteng Wang and Yanqiu Bi},
title = {GaitMAFF: Adaptive Multi-Modal Fusion of Skeleton Maps and Silhouettes for Robust Gait Recognition in Complex Scenarios},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {87},
number = {2},
pages = {22},
keywords = {Gait recognition, multi-modal fusion, adaptive feature fusion, skeleton map, silhouette},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.075704},
doi = {10.32604/cmc.2025.075704},
abstract = {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.}
}