@Article{cmc.2025.075704,
AUTHOR = {Zhongbin Luo, Zhaoyang Guan, Wenxing You, Yunteng Wang, Yanqiu Bi},
TITLE = {GaitMAFF: Adaptive Multi-Modal Fusion of Skeleton Maps and Silhouettes for Robust Gait Recognition in Complex Scenarios},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {87},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v87n2/66631},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.075704}
}