MMF-Gait: A Multi-Model Fusion-Enhanced Gait Recognition Framework Integrating Convolutional and Attention Networks
Article
Hasan, K., Tuhin, K. A., Bapary, M. R. I., Doula, M. S. U., Alam, M. A., Ahad, M. and Uddin, M. Z. 2025. MMF-Gait: A Multi-Model Fusion-Enhanced Gait Recognition Framework Integrating Convolutional and Attention Networks. Symmetry. 17 (7), p. Art. 1155. https://doi.org/10.3390/sym17071155
Authors | Hasan, K., Tuhin, K. A., Bapary, M. R. I., Doula, M. S. U., Alam, M. A., Ahad, M. and Uddin, M. Z. |
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Abstract | Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance. |
Journal | Symmetry |
Journal citation | 17 (7), p. Art. 1155 |
ISSN | 2073-8994 |
Year | 2025 |
Publisher | MDPI |
Publisher's version | License File Access Level Anyone |
Digital Object Identifier (DOI) | https://doi.org/10.3390/sym17071155 |
Publication dates | |
19 Jul 2025 | |
Publication process dates | |
Deposited | 31 Jul 2025 |
Copyright holder | © 2025 The Authors |
https://repository.uel.ac.uk/item/8zz5x
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