GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint Learning
Gait recognition, as a non-contact biometric technology, offers unique advantages in scenarios requiring long-distance identification without active cooperation from subjects. However, existing gait recognition methods predominantly rely on single-modal data, which demonstrates insufficient feature...
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2025-06-01
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| author | Siwei Wei Xiangyuan Xu Dewen Liu Chunzhi Wang Lingyu Yan Wangyu Wu |
| author_facet | Siwei Wei Xiangyuan Xu Dewen Liu Chunzhi Wang Lingyu Yan Wangyu Wu |
| author_sort | Siwei Wei |
| collection | DOAJ |
| description | Gait recognition, as a non-contact biometric technology, offers unique advantages in scenarios requiring long-distance identification without active cooperation from subjects. However, existing gait recognition methods predominantly rely on single-modal data, which demonstrates insufficient feature expression capabilities when confronted with complex factors in real-world environments, including viewpoint variations, clothing differences, occlusion problems, and illumination changes. This paper addresses these challenges by introducing a multi-modal gait recognition network based on channel shuffle regulation and spatial-frequency joint learning, which integrates two complementary modalities (silhouette data and heatmap data) to construct a more comprehensive gait representation. The channel shuffle-based feature selective regulation module achieves cross-channel information interaction and feature enhancement through channel grouping and feature shuffling strategies. This module divides input features along the channel dimension into multiple subspaces, which undergo channel-aware and spatial-aware processing to capture dependency relationships across different dimensions. Subsequently, channel shuffling operations facilitate information exchange between different semantic groups, achieving adaptive enhancement and optimization of features with relatively low parameter overhead. The spatial-frequency joint learning module maps spatiotemporal features to the spectral domain through fast Fourier transform, effectively capturing inherent periodic patterns and long-range dependencies in gait sequences. The global receptive field advantage of frequency domain processing enables the model to transcend local spatiotemporal constraints and capture global motion patterns. Concurrently, the spatial domain processing branch balances the contributions of frequency and spatial domain information through an adaptive weighting mechanism, maintaining computational efficiency while enhancing features. Experimental results demonstrate that the proposed GaitCSF model achieves significant performance improvements on mainstream datasets including GREW, Gait3D, and SUSTech1k, breaking through the performance bottlenecks of traditional methods. The implications of this research are significant for improving the performance and robustness of gait recognition systems when implemented in practical application scenarios. |
| format | Article |
| id | doaj-art-e66499a028fe4c3db75d11084c2a99a3 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-e66499a028fe4c3db75d11084c2a99a32025-08-20T03:16:39ZengMDPI AGSensors1424-82202025-06-012512375910.3390/s25123759GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint LearningSiwei Wei0Xiangyuan Xu1Dewen Liu2Chunzhi Wang3Lingyu Yan4Wangyu Wu5School of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Management, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, University of Liverpool, Liverpool L69 3DR, UKGait recognition, as a non-contact biometric technology, offers unique advantages in scenarios requiring long-distance identification without active cooperation from subjects. However, existing gait recognition methods predominantly rely on single-modal data, which demonstrates insufficient feature expression capabilities when confronted with complex factors in real-world environments, including viewpoint variations, clothing differences, occlusion problems, and illumination changes. This paper addresses these challenges by introducing a multi-modal gait recognition network based on channel shuffle regulation and spatial-frequency joint learning, which integrates two complementary modalities (silhouette data and heatmap data) to construct a more comprehensive gait representation. The channel shuffle-based feature selective regulation module achieves cross-channel information interaction and feature enhancement through channel grouping and feature shuffling strategies. This module divides input features along the channel dimension into multiple subspaces, which undergo channel-aware and spatial-aware processing to capture dependency relationships across different dimensions. Subsequently, channel shuffling operations facilitate information exchange between different semantic groups, achieving adaptive enhancement and optimization of features with relatively low parameter overhead. The spatial-frequency joint learning module maps spatiotemporal features to the spectral domain through fast Fourier transform, effectively capturing inherent periodic patterns and long-range dependencies in gait sequences. The global receptive field advantage of frequency domain processing enables the model to transcend local spatiotemporal constraints and capture global motion patterns. Concurrently, the spatial domain processing branch balances the contributions of frequency and spatial domain information through an adaptive weighting mechanism, maintaining computational efficiency while enhancing features. Experimental results demonstrate that the proposed GaitCSF model achieves significant performance improvements on mainstream datasets including GREW, Gait3D, and SUSTech1k, breaking through the performance bottlenecks of traditional methods. The implications of this research are significant for improving the performance and robustness of gait recognition systems when implemented in practical application scenarios.https://www.mdpi.com/1424-8220/25/12/3759gait recognitionGaitCSFcomputer visionpattern recognitionmulti-modaldeep learning |
| spellingShingle | Siwei Wei Xiangyuan Xu Dewen Liu Chunzhi Wang Lingyu Yan Wangyu Wu GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint Learning Sensors gait recognition GaitCSF computer vision pattern recognition multi-modal deep learning |
| title | GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint Learning |
| title_full | GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint Learning |
| title_fullStr | GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint Learning |
| title_full_unstemmed | GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint Learning |
| title_short | GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint Learning |
| title_sort | gaitcsf multi modal gait recognition network based on channel shuffle regulation and spatial frequency joint learning |
| topic | gait recognition GaitCSF computer vision pattern recognition multi-modal deep learning |
| url | https://www.mdpi.com/1424-8220/25/12/3759 |
| work_keys_str_mv | AT siweiwei gaitcsfmultimodalgaitrecognitionnetworkbasedonchannelshuffleregulationandspatialfrequencyjointlearning AT xiangyuanxu gaitcsfmultimodalgaitrecognitionnetworkbasedonchannelshuffleregulationandspatialfrequencyjointlearning AT dewenliu gaitcsfmultimodalgaitrecognitionnetworkbasedonchannelshuffleregulationandspatialfrequencyjointlearning AT chunzhiwang gaitcsfmultimodalgaitrecognitionnetworkbasedonchannelshuffleregulationandspatialfrequencyjointlearning AT lingyuyan gaitcsfmultimodalgaitrecognitionnetworkbasedonchannelshuffleregulationandspatialfrequencyjointlearning AT wangyuwu gaitcsfmultimodalgaitrecognitionnetworkbasedonchannelshuffleregulationandspatialfrequencyjointlearning |