A Survey of Human Gait Recognition for Model Free Approach
DOI:
https://doi.org/10.18486/ijcsnt/3.1.043Keywords:
Classification, Human Gait, Feature Extraction, Model Free, SilhouetteAbstract
Human gait recognition is a second era biometrics which is unpretentious and distance based. Human gait recognition is only distinguishing an individual from its strolling style. Human Cooperation is not needed in this biometric framework. There are two methodologies of gait recognition which are model based and model free methodologies. This paper gives a late exhaustive study of just model free gait recognition approach. This overview concentrates on model free gait image representation, dimensionality decrease of concentrated feature and characterization. Openly accessible gait dataset are likewise talked about. The paper is finished up by posting the examination challenges and by giving future work in model free gait recognition approach.
References
Ekinci M., “Human Identification Using Gait,” Turkish Journal of Electrical Engineering and Computer Sciences, 2006, vol. 14, no. 2. DOI: https://doi.org/10.1109/SIU.2006.1659688
Bobick A. F. and Johnson A. Y., “Gait Recognition Using Static Activity-Specific Parameters,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
Yam C. Y., Nixon M. S. and Carter J. N., “Automated Person Recognition by Walking and Running via Model-Based Approaches,” Pattern Recognition, 2003. DOI: https://doi.org/10.1016/j.patcog.2003.09.012
Online CASIA Database Information, available online.
Xu D., Huang Y., Zeng Z. et al., “Human Gait Recognition Using Patch Distribution Feature and Locality-Constrained Group Sparse Representation,” IEEE Transactions on Image Processing, 2012, vol. 21, no. 1, pp. 316–326. DOI: https://doi.org/10.1109/TIP.2011.2160956
Hu H., “Enhanced Gabor Feature-Based Classification Using a Regularized Locally Tensor Discriminant Model for Multiview Gait Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, 2013, vol. 23, no. 7, pp. 1274–1286. DOI: https://doi.org/10.1109/TCSVT.2013.2242640
Muramatsu D., Makihara Y. and Yagi Y., “Gait Recognition by Fusing Direct Cross-View Matching Scores for Criminal Investigation,” IPSJ Transactions on Computer Vision and Applications, 2013, vol. 5, pp. 35–39. DOI: https://doi.org/10.2197/ipsjtcva.5.35
Muramatsu D., Iwama H., Makihara Y. et al., “Multiview Multi-Modal Person Identification from a Single Walking Image Sequence,” Proceedings of the 6th IAPR International Conference on Biometrics (ICB 2013), Madrid, Spain, 2013, pp. 1–8. DOI: https://doi.org/10.1109/ICB.2013.6612979
Hu M., Wang Y., Zhang Z. et al., “Incremental Learning for Video-Based Gait Recognition with LBP Flow,” IEEE Transactions on Cybernetics, 2013, vol. 43, no. 1. DOI: https://doi.org/10.1109/TSMCB.2012.2199310
Iwama H., Muramatsu D., Makihara Y. et al., “Gait-Based Person Verification System for Forensics,” Proceedings of the IEEE 5th International Conference on Biometrics: Theory, Applications and Systems (BTAS 2012), Washington, D.C., USA, 2012, pp. 1–8. DOI: https://doi.org/10.1109/BTAS.2012.6374565
Hayder A., Dargham J., Chekima A. et al., “Person Identification Using Gait,” International Journal of Computer and Electrical Engineering, 2011, vol. 3, no. 4. DOI: https://doi.org/10.7763/IJCEE.2011.V3.364
Murugesan U. and Padmavathi G., “An Accurate Method for Detection of Cyber Attacks,” Australian Journal of Basic and Applied Sciences, 2013, vol. 7, no. 8, pp. 940–944, ISSN: 1991-8178.
Kawai R., Makihara Y., Hua C. et al., “Person Re-Identification Using View-Dependent Score-Level Fusion of Gait and Color Features,” Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 2012.
Mansur A., Makihara Y. and Yagi Y., “View-Invariant Gait Recognition from Low Frame-Rate Videos,” Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 2012.
Iwama H., Okumura M., Makihara Y. et al., “The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition,” IEEE Transactions on Information Forensics and Security, 2012, vol. 7, no. 5. DOI: https://doi.org/10.1109/TIFS.2012.2204253
Makihara Y., Rossa B. S. and Yagi Y., “Gait Recognition Using Images of Oriented Smooth Pseudo Motion,” 2012 IEEE International Conference on Systems, Man, and Cybernetics, COEX, Seoul, Korea, 2012. DOI: https://doi.org/10.1109/ICSMC.2012.6377914
Bashir K., Xiang T. and Gong S., “Gait Recognition without Subject Cooperation,” Pattern Recognition Letters, 2010, vol. 31, no. 13, pp. 2052–2060. DOI: https://doi.org/10.1016/j.patrec.2010.05.027
Makihara Y., Muramatsu D., Iwama H. et al., “On Combining Gait Features,” Proceedings of the 10th IEEE Conference on Automatic Face and Gesture Recognition (FG 2013), Shanghai, China, 2013, pp. 1–8. DOI: https://doi.org/10.1109/FG.2013.6553797
Wang J., She M., Nahavandi S. et al., “A Review of Vision-Based Gait Recognition Methods for Human Identification,” Digital Image Computing: Techniques and Applications, 2010. DOI: https://doi.org/10.1109/DICTA.2010.62
Ali H., Dargham J., Ali C. et al., “Gait Recognition Using Principal Component Analysis,” Proceedings of the 3rd International Conference on Machine Vision (ICMV 2010), 2010.
Roy A., Sural S., Mukherjee J. et al., “Occlusion Detection and Gait Silhouette Reconstruction from Degraded Scenes,” Signal, Image and Video Processing, 2011, vol. 5, no. 4, pp. 415–430. DOI: https://doi.org/10.1007/s11760-011-0245-5
Hofmann M., Sural S. and Rigoll G., “Gait Recognition in the Presence of Occlusion: A New Dataset and Baseline Algorithms.”
Piccardi M., “Background Subtraction Techniques: A Review,” 2004 IEEE International Conference on Systems, Man and Cybernetics, 2004.
Murukesh C. and Thanushkodi K., “An Efficient Gait Recognition System Based on PCA and Multi-Layer Perceptron,” Life Science Journal, 2013, vol. 10, no. 7s.
Huang Y., Xu D. and Cham T. J., “Face and Human Gait Recognition Using Image-to-Class Distance,” IEEE Transactions on Circuits and Systems for Video Technology, 2010, vol. 20, no. 3. DOI: https://doi.org/10.1109/TCSVT.2009.2035852
Hosseini N. K. and Nordin M. J., “Human Gait Recognition: A Silhouette-Based Approach,” Journal of Automation and Control Engineering, 2013, vol. 1, no. 2. Online resource, University of Córdoba. DOI: https://doi.org/10.12720/joace.1.2.103-105
Chen J. and Liu J., “Average Gait Differential Image-Based Human Recognition,” The Scientific World Journal, Hindawi Publishing Corporation, 2014. Online resource, Biometrics Research. DOI: https://doi.org/10.1155/2014/262398
Uddin M. Z., Kim J. T. and Kim T. S., “Depth Video-Based Gait Recognition for Smart Home Using Local Directional Pattern Features and Hidden Markov Model,” 2014. Online resource, University of Texas–Pan American. DOI: https://doi.org/10.1177/1420326X14522670
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