Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait Recognition
Gait recognition, a form of biometric authentication, facilitates the identification of individuals by analyzing their characteristic walking patterns. This approach exhibits superior performance even from distant, low-resolution imagery from security camera footage. Historically, gait recognition m...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10786216/ |
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| author | Md. Zasim Uddin Kamrul Hasan Md Atiqur Rahman Ahad Fady Alnajjar |
| author_facet | Md. Zasim Uddin Kamrul Hasan Md Atiqur Rahman Ahad Fady Alnajjar |
| author_sort | Md. Zasim Uddin |
| collection | DOAJ |
| description | Gait recognition, a form of biometric authentication, facilitates the identification of individuals by analyzing their characteristic walking patterns. This approach exhibits superior performance even from distant, low-resolution imagery from security camera footage. Historically, gait recognition methodologies used the entire sequence of a human body silhouette for spatiotemporal characterization. Recent advancements have introduced part-based feature extraction modules derived from the human body’s transverse plane (i.e., horizontal direction) into cross-view gait recognition (CVGR) applications. However, this study reveals the considerable potential of the parts in the sagittal plane (i.e., vertical direction) to enhance discrimination in CVGR. A novel method is proposed that integrates the parts generated according to transverse and sagittal planes utilizing three-dimensional and two-dimensional convolutional neural networks for robust feature extraction. The proposed method comprises a global, horizontal, and vertical part module for capturing fine-grained local details in the horizontal and vertical part directions, and a horizontal and vertical pyramid mapping module for extracting spatial features into the horizontal and vertical pyramid mapping. The consolidated features from both modules enhance CVGR performance, even amidst challenging covariates such as different carried objects and clothing variations, along with uncontrolled walking patterns in the wild. The effectiveness of this method is demonstrated through its implementation on the CASIA-B, OU-MVLP, and Gait3D benchmark datasets, where it exhibits superior gait recognition performance. |
| format | Article |
| id | doaj-art-4ef8f84f258d46d5a4073f1a89ccb5e6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4ef8f84f258d46d5a4073f1a89ccb5e62024-12-14T00:01:25ZengIEEEIEEE Access2169-35362024-01-011218551118552710.1109/ACCESS.2024.351354110786216Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait RecognitionMd. Zasim Uddin0https://orcid.org/0009-0004-9334-4896Kamrul Hasan1https://orcid.org/0009-0006-8509-7957Md Atiqur Rahman Ahad2https://orcid.org/0000-0001-8355-7004Fady Alnajjar3https://orcid.org/0000-0001-6102-3765Department of Computer Science and Engineering, Begum Rokeya University, Rangpur, BangladeshDepartment of Computer Science and Engineering, Begum Rokeya University, Rangpur, BangladeshDepartment of Computer Science and Digital Technologies, University of East London, London, U.K.Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab EmiratesGait recognition, a form of biometric authentication, facilitates the identification of individuals by analyzing their characteristic walking patterns. This approach exhibits superior performance even from distant, low-resolution imagery from security camera footage. Historically, gait recognition methodologies used the entire sequence of a human body silhouette for spatiotemporal characterization. Recent advancements have introduced part-based feature extraction modules derived from the human body’s transverse plane (i.e., horizontal direction) into cross-view gait recognition (CVGR) applications. However, this study reveals the considerable potential of the parts in the sagittal plane (i.e., vertical direction) to enhance discrimination in CVGR. A novel method is proposed that integrates the parts generated according to transverse and sagittal planes utilizing three-dimensional and two-dimensional convolutional neural networks for robust feature extraction. The proposed method comprises a global, horizontal, and vertical part module for capturing fine-grained local details in the horizontal and vertical part directions, and a horizontal and vertical pyramid mapping module for extracting spatial features into the horizontal and vertical pyramid mapping. The consolidated features from both modules enhance CVGR performance, even amidst challenging covariates such as different carried objects and clothing variations, along with uncontrolled walking patterns in the wild. The effectiveness of this method is demonstrated through its implementation on the CASIA-B, OU-MVLP, and Gait3D benchmark datasets, where it exhibits superior gait recognition performance.https://ieeexplore.ieee.org/document/10786216/Gait recognitiondeep learningglobal and local Part-basedhorizontal and vertical part-basedhorizontal and vertical pyramid mapping |
| spellingShingle | Md. Zasim Uddin Kamrul Hasan Md Atiqur Rahman Ahad Fady Alnajjar Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait Recognition IEEE Access Gait recognition deep learning global and local Part-based horizontal and vertical part-based horizontal and vertical pyramid mapping |
| title | Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait Recognition |
| title_full | Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait Recognition |
| title_fullStr | Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait Recognition |
| title_full_unstemmed | Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait Recognition |
| title_short | Horizontal and Vertical Part-Wise Feature Extraction for Cross-View Gait Recognition |
| title_sort | horizontal and vertical part wise feature extraction for cross view gait recognition |
| topic | Gait recognition deep learning global and local Part-based horizontal and vertical part-based horizontal and vertical pyramid mapping |
| url | https://ieeexplore.ieee.org/document/10786216/ |
| work_keys_str_mv | AT mdzasimuddin horizontalandverticalpartwisefeatureextractionforcrossviewgaitrecognition AT kamrulhasan horizontalandverticalpartwisefeatureextractionforcrossviewgaitrecognition AT mdatiqurrahmanahad horizontalandverticalpartwisefeatureextractionforcrossviewgaitrecognition AT fadyalnajjar horizontalandverticalpartwisefeatureextractionforcrossviewgaitrecognition |