- Volume 14 Issue 4
DOI QR Code
Gait Recognition Algorithm Based on Feature Fusion of GEI Dynamic Region and Gabor Wavelets
- Huang, Jun (College of Modern Science and Technology, China Jiliang University) ;
- Wang, Xiuhui (College of Information Engineering, China Jiliang University) ;
- Wang, Jun (College of Information Engineering, China Jiliang University)
- Received : 2017.08.17
- Accepted : 2017.11.28
- Published : 2018.08.31
The paper proposes a novel gait recognition algorithm based on feature fusion of gait energy image (GEI) dynamic region and Gabor, which consists of four steps. First, the gait contour images are extracted through the object detection, binarization and morphological process. Secondly, features of GEI at different angles and Gabor features with multiple orientations are extracted from the dynamic part of GEI, respectively. Then averaging method is adopted to fuse features of GEI dynamic region with features of Gabor wavelets on feature layer and the feature space dimension is reduced by an improved Kernel Principal Component Analysis (KPCA). Finally, the vectors of feature fusion are input into the support vector machine (SVM) based on multi classification to realize the classification and recognition of gait. The primary contributions of the paper are: a novel gait recognition algorithm based on based on feature fusion of GEI and Gabor is proposed; an improved KPCA method is used to reduce the feature matrix dimension; a SVM is employed to identify the gait sequences. The experimental results suggest that the proposed algorithm yields over 90% of correct classification rate, which testify that the method can identify better different human gait and get better recognized effect than other existing algorithms.
- N. V. Boulgouris, D. Hatzinakos, and K. N. Plataniotis, "Gait recognition: a challenging signal processing technology for biometric identification," IEEE Signal Processing Magazine, vol. 22, no. 6, pp. 78-90, 2005. https://doi.org/10.1109/MSP.2005.1550191
- I. Bouchrika and M. S. Nixon, "Model-based feature extraction for gait analysis and recognition," in Computer Vision/Computer Graphics Collaboration Techniques and Applications. Heidelberg: Springer, 2007, pp. 150-160.
- C. Wang, J. Zhang, L. Wang, J. Pu, and X. Yuan, "Human identification using temporal information preserving gait template," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2164-2176, 2012. https://doi.org/10.1109/TPAMI.2011.260
- J. Han and B. Bhanu, "Individual recognition using gait energy image," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. 2, pp. 316-322, 2006. https://doi.org/10.1109/TPAMI.2006.38
- Y. Makihara, T. Tanoue, D. Muramatsu, Y. Yagi, S. Mori, Y. Utsumi, M. Iwamura, and K. Kise, "Individualitypreserving silhouette extraction for gait recognition," IPSJ Transactions on Computer Vision and Applications, vol. 7, pp. 74-78, 2015. https://doi.org/10.2197/ipsjtcva.7.74
- E. Zhang, Y. Zhao, and W. Xiong, "Active energy image plus 2DLPP for gait recognition," Signal Processing, vol. 90, no. 7, pp. 2295-2302, 2010. https://doi.org/10.1016/j.sigpro.2010.01.024
- Y. Li and K. Li, "Gait recognition based on dual view and multiple feature information fusion," CAAI Transactions on Intelligent Systems, vol. 8, no. 1, pp. 74-79, 2013.
- J. Wu, J. Wang, and L. Liu, "Kernel-based method for automated walking patterns recognition using kinematics data," in Advances in Natural Computation. Heidelberg: Springer, pp. 560-569.
- Q. Yang and K. Qiu, "Gait recognition based on active energy image and parameter-adaptive kernel PCA," in Proceedings of 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, 2011, pp. 156-159.
- S. Fazli, H. Askarifar, and M. J. Tavassoli, "Gait recognition using SVM and LDA," in Proceedings of International Conference on Advances in Computing, Control, and Telecommunication Technologies, Jakarta, Indonesia, 2011, pp. 106-109.
- L. Qiao, S. Chen, and X. Tan, "Sparsity preserving projections with applications to face recognition," Pattern Recognition, vol. 43, no. 1, pp. 331-341, 2010. https://doi.org/10.1016/j.patcog.2009.05.005
- K. Wang, T. Yan, Z. Lu, and M. Tang, "Kernel sparsity preserving projections and its application to gait recognition," Journal of Image and Graphics, vol. 18, no. 3, pp. 257-263, 2013.
- R. Atta, S. Shaheen, and M. Ghanbari, "Human identification based on temporal lifting using 5/3 wavelet filters and radon transform," Pattern Recognition, vol. 69, pp. 213-224, 2017. https://doi.org/10.1016/j.patcog.2017.04.015
- J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, "Two-dimensional PCA: a new approach to appearance-based face representation and recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131-137, 2004. https://doi.org/10.1109/TPAMI.2004.1261097
- D. Y. Huang, T. W. Lin, W. C. Hu, and C. H. Cheng, "Gait recognition based on Gabor wavelets and modified gait energy image for human identification," Journal of Electronic Imaging, vol. 22, no. 4, article no. 043039, 2013.
- B. A. Abdullah and E. S. M. El-Alfy, "Statistical Gabor-based gait recognition using region-level analysis," in Proceedings of 2015 IEEE European Modelling Symposium (EMS), Madrid, Spain, 2015, pp. 137-141.
- H. Shao and Y. Wang, "Gait recognition method based on integrated Gabor feature," Journal of Electronic Measurement and Instrumentation, vol. 31, no. 4, pp. 573-579, 2017.
- X. Wang, J. Wang, and K. Yan, "Gait recognition based on Gabor wavelets and (2D)2PCA," Multimedia Tools and Applications, vol. 77, no 10, pp. 12545-12561, 2018. https://doi.org/10.1007/s11042-017-4903-7
- S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K. W. Bowyer, "The humanID gait challenge problem: data sets, performance, and analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 162-177, 2005. https://doi.org/10.1109/TPAMI.2005.39
- D. H. Liu, K. M. Lam, and L. S. Shen, "Optimal sampling of Gabor features for face recognition," Pattern Recognition Letters, vol. 25, no. 2, pp. 267-276, 2004. https://doi.org/10.1016/j.patrec.2003.10.007
- T. S. Lee, "Image representation using 2D Gabor wavelets," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp. 959-971, 1996. https://doi.org/10.1109/34.541406
- Center for Biometrics and Security Research, "CASIA Gait Database," 2005 [Online]. Available: http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp.
- D. M. Tax, M. Van Breukelen, R. P. Duin, and J. Kittler, "Combining multiple classifiers by averaging or by multiplying?," Pattern Recognition, vol. 33, no. 9, pp. 1475-1485, 2000. https://doi.org/10.1016/S0031-3203(99)00138-7
- K. Yan, Z. Ji, and W. Shen, "Online fault detection methods for chillers combining extended Kalman filter and recursive one-class SVM," Neurocomputing, vol. 228, pp. 205-212, 2017. https://doi.org/10.1016/j.neucom.2016.09.076
- D. K. Vishwakarma and K. Singh, "Human activity recognition based on spatial distribution of gradients at sublevels of average energy silhouette images," IEEE Transactions on Cognitive and Developmental Systems, vol. 9, no. 4, pp. 316-327, 2017. https://doi.org/10.1109/TCDS.2016.2577044
- P. Liu, "Gait recognition method based on Poisson distribution on Gabor wavelet," Computer Engineering and Applications, vol. 51(Suppl), pp. 1-5, 2015.
- Y. Ji, H. Zhao, and X. Zhang, "A feature fusion based gait recognition algorithm," Journal of Electrical & Electronic Education, vol. 3, no. 5, pp. 67-70, 2009.
- H. Chen H and Z. Cao, "Front-view gait recognition based on the fusion of static and dynamic features," Opto-Electronic Engineering, vol. 41, pp. 83-88, 2013.