GaitRGA: Gait Recognition Based on Relation-Aware Global Attention
Gait recognition, a long-range biometric technique based on walking posture, the fact that they do not require the cooperation of the subject and are non-invasive has made them highly sought after in recent years.Although existing methods have achieved impressive results in laboratory environments,...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2337 |
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| author | Jinhang Liu Yunfan Ke Ting Zhou Yan Qiu Chunzhi Wang |
| author_facet | Jinhang Liu Yunfan Ke Ting Zhou Yan Qiu Chunzhi Wang |
| author_sort | Jinhang Liu |
| collection | DOAJ |
| description | Gait recognition, a long-range biometric technique based on walking posture, the fact that they do not require the cooperation of the subject and are non-invasive has made them highly sought after in recent years.Although existing methods have achieved impressive results in laboratory environments, the recognition performance is still deficient in real-world applications, especially when confronted with complex and dynamic scenarios. The major challenges in gait recognition include changes in viewing angle, occlusion, clothing changes, and significant differences in gait characteristics under different walking conditions. To slove these issues, we propose a gait recognition method based on relational-aware global attention. Specifically, we introduce a Relational-aware Global Attention (RGA) module, which captures global structural information within gait sequences to enable more precise attention learning. Unlike traditional gait recognition methods that rely solely on local convolutions, we stack pairwise associations between each feature position in the gait silhouette and all other feature positions, along with the features themselves, using a shallow convolutional model to learn attention. This approach is particularly effective in gait recognition due to the physical constraints on human walking postures, allowing the structural information embedded in the global relationships to aid in inferring the semantics and focus areas of various body parts, thereby improving the differentiation of gait features across individuals. Our experimental results on multiple datasets (Grew, Gait3D, SUSTech1k) demonstrate that GaitRGA achieves significant performance improvements, especially in real-world scenarios. |
| format | Article |
| id | doaj-art-8d13b5180a354801879b8bf9b5e6a42c |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-8d13b5180a354801879b8bf9b5e6a42c2025-08-20T02:25:12ZengMDPI AGSensors1424-82202025-04-01258233710.3390/s25082337GaitRGA: Gait Recognition Based on Relation-Aware Global AttentionJinhang Liu0Yunfan Ke1Ting Zhou2Yan Qiu3Chunzhi Wang4School of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaDepartment of Computer Science, College of Engineering and Technology, Hubei University of Technology, Wuhan 430068, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan 430068, ChinaGait recognition, a long-range biometric technique based on walking posture, the fact that they do not require the cooperation of the subject and are non-invasive has made them highly sought after in recent years.Although existing methods have achieved impressive results in laboratory environments, the recognition performance is still deficient in real-world applications, especially when confronted with complex and dynamic scenarios. The major challenges in gait recognition include changes in viewing angle, occlusion, clothing changes, and significant differences in gait characteristics under different walking conditions. To slove these issues, we propose a gait recognition method based on relational-aware global attention. Specifically, we introduce a Relational-aware Global Attention (RGA) module, which captures global structural information within gait sequences to enable more precise attention learning. Unlike traditional gait recognition methods that rely solely on local convolutions, we stack pairwise associations between each feature position in the gait silhouette and all other feature positions, along with the features themselves, using a shallow convolutional model to learn attention. This approach is particularly effective in gait recognition due to the physical constraints on human walking postures, allowing the structural information embedded in the global relationships to aid in inferring the semantics and focus areas of various body parts, thereby improving the differentiation of gait features across individuals. Our experimental results on multiple datasets (Grew, Gait3D, SUSTech1k) demonstrate that GaitRGA achieves significant performance improvements, especially in real-world scenarios.https://www.mdpi.com/1424-8220/25/8/2337GaitRGAbiometric identificationsilhouette-based Gait recognitiondeep learningneural network |
| spellingShingle | Jinhang Liu Yunfan Ke Ting Zhou Yan Qiu Chunzhi Wang GaitRGA: Gait Recognition Based on Relation-Aware Global Attention Sensors GaitRGA biometric identification silhouette-based Gait recognition deep learning neural network |
| title | GaitRGA: Gait Recognition Based on Relation-Aware Global Attention |
| title_full | GaitRGA: Gait Recognition Based on Relation-Aware Global Attention |
| title_fullStr | GaitRGA: Gait Recognition Based on Relation-Aware Global Attention |
| title_full_unstemmed | GaitRGA: Gait Recognition Based on Relation-Aware Global Attention |
| title_short | GaitRGA: Gait Recognition Based on Relation-Aware Global Attention |
| title_sort | gaitrga gait recognition based on relation aware global attention |
| topic | GaitRGA biometric identification silhouette-based Gait recognition deep learning neural network |
| url | https://www.mdpi.com/1424-8220/25/8/2337 |
| work_keys_str_mv | AT jinhangliu gaitrgagaitrecognitionbasedonrelationawareglobalattention AT yunfanke gaitrgagaitrecognitionbasedonrelationawareglobalattention AT tingzhou gaitrgagaitrecognitionbasedonrelationawareglobalattention AT yanqiu gaitrgagaitrecognitionbasedonrelationawareglobalattention AT chunzhiwang gaitrgagaitrecognitionbasedonrelationawareglobalattention |