The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Fusion of Static and Dynamic Body Biometrics for Gait Recognition
Abstract
Vision-based human identification at a distance has recently gained growing interest from computer vision researchers. This paper describes a human recognition algorithm by combining static and dynamic body biometrics. For each sequence involving a walker, temporal pose changes of the segmented moving silhouettes are represented as an associated sequence of complex vector configurations and are then analyzed using the Procrustes shape analysis method to obtain a compact appearance representation, called static information of body. In addition, a model-based approach is presented under a Condensation framework to track the walker and to further recover joint-angle trajectories of lower limbs, called dynamic information of gait. Both static and dynamic cues obtained from walking video may be independently used for recognition using the nearest exemplar classifier. They are fused on the decision level using different combinations of rules to improve the performance of both identification and verification. Experimental results of a dataset including 20 subjects demonstrate the feasibility of the proposed algorithm.
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Cites
Kittler, J., Hatef, M., Duin, R. P. W., & Matas, J. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.667881 · Cited by 5,290
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
The FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
Recent developments in human motion analysis
Wang, L., Hu, W., & Tan, T. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(02)00100-0 · Cited by 962
Self-citationGait analysis for recognition and classification
Lee, L., & Grimson, W. E. L. (2003) · Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition · doi:10.1109/afgr.2002.1004148 · Cited by 734
Person identification using multiple cues
Brunelli, R., & Falavigna, D. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.464560 · Cited by 592
Integrating faces and fingerprints for personal identification
Hong, L., & Jain, A. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.735803 · Cited by 581
Analyzing and recognizing walking figures in XYT
Niyogi, & Adelson (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323868 · Cited by 572
Silhouette-based human identification from body shape and gait
Collins, R. T., Gross, R., & Shi, J. (2003) · Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition · doi:10.1109/afgr.2002.1004181 · Cited by 500
Recognizing People by Their Gait : The Shape of Motion
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Cited by
Yu, S., Tan, D., & Tan, T. (2006) · 18th International Conference on Pattern Recognition (ICPR'06) · doi:10.1109/icpr.2006.67 · Cited by 1,056
Self-citation(2019) · ACM Computing Surveys · doi:10.1145/3230633 · Cited by 303
Gait Components and Their Application to Gender Recognition
Li, X., Maybank, S. J., Yan, S., Tao, D., & Xu, D. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.913886 · Cited by 300
A Study on Gait-Based Gender Classification
Yu, S., Tan, T., Huang, K., Jia, K., & Wu, X. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2020535 · Cited by 293
Self-citationRobust gait recognition: a comprehensive survey
Rida, I., Almaadeed, N., & Almaadeed, S. (2019) · IET Biometrics · doi:10.1049/iet-bmt.2018.5063 · Cited by 119
Metadata
- Authors
- Wang, L., Ning, H., Tan, T., & Hu, W.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2004-02
- Volume
- 14
- Issue
- 2
- Pages
- 149-158
- ISSN
- 1051-8215 (ISSN-L)
- OpenAlex
- W1979856851
- Cited by
- 377
- References
- 27
- FWCI
- 12.62
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, L., Ning, H., Tan, T., & Hu, W. (2004). Fusion of Static and Dynamic Body Biometrics for Gait Recognition. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/tcsvt.2003.821972
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