Support Vector Machine vs Pattern Network for Gait Recognition

Shahla A. AbdAlKader

Abstract


Human recognition based on biometric information is important due to its reliability in identity verification. Gait recognition has ability to recognize individuals from a distance. This study includes human gait recognition based firstly on support vector machine (SVM) and secondly on PatternNet neural network. Experiments were conducted with comparisons based on the two approaches. Experimental results showed that the PattenNet neural network is more effective than the SVM in gait recognition.

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References


Yanbei Li, Lei Yan and Hua Qian, A Gait Recognition System using GA-based C-SVC and Plantar Pressure, TELKOMNIKA, Vol. 11, No. 10, October 2013, pp. 6135 -6142e-ISSN: 2087-278X

Fong-Chin Su and Wen-Lan Wu, Design and testing of a genetic algorithm neural network in the assessment of gait patterns, Medical Engineering & Physics 22 (2000) 67–74, Technical note.

Parneet Kaur, Gait Recognition For Human Identification Using ENN And NN, Proceedings of SARC-IRAJ International Conference, 14th July 2013, Delhi, India, ISBN: 978-93-82702-21-4

Pratibha Mishra and Shweta Ezra, Human Gait Recognition Using Bezier Curves, International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 2 Feb 2011, pp:969-975, ISSN : 0975-3397.

Saeid Fazli, et al. Gait Recognition using SVM and LDA, Proc. of Int. Conf. on Advances in Computing, Control, and Telecommunication Technologies 2011, 2011 ACEEE DOI: 03.CSS.2011.1. Proc. of Int. Conf. on Advances in Computing, Control, and Telecommunication Technologies 2011, 42, pp:106-109.

Hadi S. Yazdi, et al, Gait Recognition Based on Invariant Leg Classification Using a Neuro-Fuzzy Algorithm as the FusionMethod, International Scholarly Research Network, ISRN Artificial Intelligence, Volume 2012, Article ID 289721, doi:10.5402/2012/289721

WEN-LAN WU, et al, Potential of the Genetic Algorithm Neural Network in the Assessment of Gait Patterns in Ankle Arthrodesis, Annals of Biomedical Engineering, Vol. 29, pp. 83–91, 2001, pp:83-91.

Zhaoxiang Zhang, et al, A Survey of Advances in Biometric Gait Recognition, Biometric Recognition, Lecture Notes in Computer Science Volume 7098, 2011, pp 150-158.

Jin Wang, et al , A Review of Vision-based Gait Recognition Methods for Human Identification, 2010 Digital Image Computing: Techniques and Applications,

Jang-Hee Yoo, et al. Automated Human Recognition by Gait using Neural Network, Image Processing Theory, Tools & Applications, IEEE, 2008.

Saeid Fazli, et al. Multi-View Neural Network Based Gait Recognition, World Academy of Science, Engineering and Technology, Vol:4 2010-07-24, pp:513-517.

Sanjeev Sharma, et al. Identification of People Using Gait Biometrics, International Journal of Machine Learning and Computing, Vol. 1, No. 4, October 2011

G. Venkata Narasimhulu and S. A. K. Jilani, Back Propagation Neural Network based Gait Recognition, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (5) , 2012,5025 – 5030.

O. Sharma and S. Bansal Gait Recogniton System for Human Identification Using BPNN Classifier, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-3, Issue-1, June 2013.

Ira Gaba and Paramjit Kaur, Biometric Identification on The Basis of BPNN Classifier with Other Novel Techniques Used For Gait Analysis, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, Issue-4, September 2013.

Parneet Kaur, Gait Recognition for Human Identification Using ENN And NN, Proceedings of SARC-IRAJ International Conference, 14th July 2013, Delhi, India, ISBN: 978-93-82702-21-4

Aditya Krishna Menon, Large-Scale Support Vector Machines: Algorithms and Theory

L. Lee, W.E.L. Grimson, Gait analysis for recognition and classification, in: IEEE International Conference on Automatic Face& Gesture Recognition (FG),2002, pp. 155–162.

L. Walawalkar, et al. Support vector learning for gender classification using audio and visual cues, Int. J. Pattern Recognition Artif. Intell. 17 (3) (2003)417–439.

G. Shan,S. Gong and P W. McOwan ,” Fusing gait and facecues for human gender recognition”, Neuro computing (2008).[17] C. Hsu, C. Lin (2002) “A Comparison of Methods for Multiclass Support Vector Machines”, IEEE Trans. NEURALNETWORKS, Vol. 13, NO.2, MARCH 2000.

Ricardo Gutierrez-Osuna, CSCE 666 Pattern Analysis, L10: Linear discriminants analysis, CSE@TAMU.

Jang-Hee Yoo et al, Gender Classification in Human Gait Using Support Vector Machine, J. Blanc-Talon et al. (Eds.): ACIVS 2005, LNCS 3708, pp. 138 – 145, 2005.© Springer-Verlag Berlin Heidelberg 2005.

Arun Joshi1, et al. Gait Recognition Of Human Using Svm And Bpnn ClassifierS, International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 3, Issue. 1, January 2014, pg.281 – 290.

MathLib Statistics Toolbox™ User's Guide R2014b, The MathWorks, Inc., 2014.

CASIA Gait Database, http:// www.sinobiometrics.com, 2006.




DOI: https://doi.org/10.23956/ijarcsse/V7I7/01705

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