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Analysis of Human Gait for Person Identification and Human Action Recognition

Deepak N.A., Sinha U.N.. Published in Pattern Recognition.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Deepak N.A., Sinha U.N.

Deepak N.A. and Sinha U.N.. Article: Analysis of Human Gait for Person Identification and Human Action Recognition. Communications on Applied Electronics 4(4):1-4, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Deepak N.A. and Sinha U.N.},
	title = {Article: Analysis of Human Gait for Person Identification and Human Action Recognition},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {4},
	pages = {1-4},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


The gait sequence analysis finds applications in several fields such as, person identification, human action recognition and event classification. The extraction of gait sequences is the initial step in gait sequence analysis. The Latent Dirichlet Allocation (LDA) a type of ’Topic Model’ is used to analyse the gait sequences for person identification and human action recognition. This is achieved by proposing a novel method that transforms the gait sequences into words representation. Finally, the generated words are then analysed using LDA for person identification and human action recognition. The proposed person identification algorithm is tested using CASIA gait dataset A and human action recognition algorithm is tested using Weizmann action recognition dataset, resulting in correct classification rate of 85%using CASIA Dataset A and 85% using Weizmann action recognition dataset.


  1. Dong Ming, Cong Zhang, Yanru Bai, Baikun Wan, Yong Hu and KDK Luk, ”Gait Recognition Based on Multiple Views Fusion of Wavelet Descriptor and Human Skeleton Model”, IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems, pp. 246-249, 2009.
  2. Jinyan Chen, Jiansheng Liu, ”Average Gait Differential Image Based Human Recognition”, The Scientific World Journal, vol. 2014, pp. 1-8, 2014.
  3. Jinyan Chen, ”Gait Correlation Analysis Based Human Identification”, Scientific World Journal, vol. 16, pp. 275- 283, 2014.
  4. Muhammad Rasyid Aqmar, Koichi Shinoda and Sadaoki Furui, ”Efficient Model Training for HMM-based Person Identification by Gait”, IEEE Signal and Information Processing, pp. 1-4, 2012.
  5. Chiraz BenAbdelkader, Ross Cutler and Larry Davis, ”View-Invariant Estimation of Height and Stride for Gait Recognition”, Biometric Authentication, European Conference on Computer Vision: International. Workshop, vol. 4, pp. 155-167, 2002.
  6. I. Laptev and P. Prez, ”Retrieving actions in Movies”, IEEE International Conference on Conference Vision, 2007.
  7. H. Wang, J.C. Niebles and L.Fei-Fei, ”Unsupervised learning of human action categories using spatial temporal words”, International Journals on Computers, vol. 79(3), pp. 299-318, 2008.
  8. Hu Ng, Hau-Lee Tong, Wooi-Haw Tan and Timothy Tzen- Vun Yap, ”Human Identification Based on Extracted Gait Features”, International Journal on New Computer Architectures and Their Applications, vol. 1, pp. 358-370, 2011.
  9. Emdad Hossain and Girija Chetty, ”Person Identification in Surveillance Video Using Gait Biometric cues”, International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1877-1881, 2012.
  10. Daigo Muramatsu, Akira Shiraishi, Yasushi Makihara, Md. Zasim Uddin and Yasushi Yagi, ”Gait-Based Person Recognition using Arbitrary View Transformation Model”, IEEE Transactions on Image Processing, vol. 2, pp. 140-154, 2015.
  11. Chunsheng HUA, Yasushi Makihara and Yasushi Yagi, ”Pedestrian Detection by using Spatio Temporal Histogram of Oriented Gradients”, IEICE Transaction. Information & System, vol. 96, pp. 1376-1386, 2013.
  12. Jiwen Lu and Erhu Zhang, ”Gait Recognition for Human Identification based on ICA and Fuzzy SVM Through Multiple Views Fusion”, IEEE Pattern Recognition Letters, vol. 28, pp. 2401-2411, 2007.
  13. Liang Wang, Tieniu Tan, Huazhong Ning and Weiming Hu, ”Silhouette Analysis-Based Gait Recognition for Human Identification, ” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1505-1518, 2003.
  14. Murat Ekinci, ” A New Attempt to Silhouette-Based Gait Recognition for Human Identification”, Computer Science Journals, vol. 4013, pp. 443-454, 2006.
  15. Y. Dedeolu, ”Human action recognition using Gaussian mixture model based background segmentation”, vol. 1(1), pp. 1-9, 2009.
  16. G. Zhao,V. Kellokumpu and M. Pietikinen, ”Human activity recognition using a dynamic texture based method”, IEEE Transactions on Pattern Analysis and Machine Intelligence Journal, vol. 32(9), pp. 1705-1720, 2010.
  17. T. Guha and Rabak. K Ward, ”Learning sparse representations for human action recognition”, IEEE Transaction, Pattern Analysis Machine Intelligence, vol. 34(8), pp. 1576- 1588, 2012.
  18. Andrew Y. Nag, D.M. Blei and Michael. I. Jordan, ”Latent Dirichlet Allocation”, Journal of Machine Learning Research, pp. 993-1022, 2003.
  19. Changhong Chen, Jimin Liang and Xiuchang Zhu, ”Gait Recognition Based on Improved Dynamic Bayesian Networks”, IEEE Pattern Recognition Letters, vol. 44, pp. 988- 995, 2011.


Binary Silhouettes, Gait Analysis, Images-Topics-Words, Transformation, Latent Dirichlet Allocation