International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.6, No.3, Feb. 2014

Gender Classification Method Based on Gait Energy Motion Derived from Silhouette Through Wavelet Analysis of Human Gait Moving Pictures

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Kohei Arai, Rosa Andrie Asmara

Index Terms

Gender Classification, Human Gait, Gait Energy Motion, Wavelet Analysis


Gender classification method based on Gait Energy Motion: GEM derived through wavelet analysis of human gait moving pictures is proposed. Through experiments with human gait moving pictures, it is found that the extracted features of wavelet coefficients using silhouettes images are useful for improvement of gender classification accuracy. Also, it is found that the proposed gender classification method shows the best classification performance, 97.63% of correct classification ratio.

Cite This Paper

Kohei Arai, Rosa Andrie Asmara,"Gender Classification Method Based on Gait Energy Motion Derived from Silhouette Through Wavelet Analysis of Human Gait Moving Pictures", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.3, pp.1-11, 2014. DOI: 10.5815/ijitcs.2014.03.01


[1]X. Qinghan, Technology review – Biometrics Technology, Application, Challenge, and Computational Intelligence Solutions, IEEE Computational Intelligence Magazine, vol. 2, pp. 5-25, 2007.

[2]Jin Wang, Mary She, Saeid Nahavandi, Abbas Kouzani, “A Review of Vision-based Gait Recognition Methods for Human Identification”, IEEE Computer Society, 2010 International Conference on Digital Image Computing: Techniques and Applications, pp. 320 - 327, 2010

[3]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, pp. 78-90, 2005.

[4]M. S. Nixon and J. N. Carter, "Automatic Recognition by Gait", Proceedings of the IEEE, vol. 94, pp. 2013-2024, 2006.

[5]Y. Jang-Hee, H. Doosung, M. Ki-Young, and M. S. Nixon, “Automated Human Recognition by Gait using Neural Network”, in First Workshops on Image Processing Theory, Tools and Applications, 2008, pp. 1-6.

[6]Wilfrid Taylor Dempster, George R. L. Gaughran, “Properties of Body Segments Based on Size and Weight”, American Journal of Anatomy, Volume 120, Issue 1, pages 33–54, January 1967. 

[7]Gilbert Strang and Truong Nguen, Wavelets and Filter Banks. Wellesley-Cambridge Press, MA, 1997, pp. 174-220, 365-382

[8]I. Daubechies, Ten lectures on wavelets, Philadelphis, PA: SIAM, 1992.

[9]CASIA Gait Database, English/index.asp

[10]Edward WONG Kie Yih, G. Sainarayanan, Ali Chekima, "Palmprint Based Biometric System: A Comparative Study on Discrete Cosine Transform Energy, Wavelet Transform Energy and Sobel Code Methods", Biomedical Soft Computing and Human Sciences, Vol.14, No.1, pp.11-19, 2009

[11]Dong Xu, Shuicheng Yan, Dacheng Tao, Stephen Lin, and Hong-Jiang Zhang, Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content- Based Image Retrieval, IEEE Transactions On Image Processing, Vol. 16, No. 11, November 2007 

[12]Hui-Yu Huang, Shih-Hsu Chang, A lossless data hiding based on discrete Haar wavelet transform, 10th IEEE International Conference on Computer and Information Technology, 2010

[13]Kiyoharu Okagaki, Kenichi Takahashi, Hiroaki Ueda, Robustness Evaluation of Digital Watermarking Based on Discrete Wavelet Transform, Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2010

[14]Bogdan Pogorelc, Matjaž Gams, Medically Driven Data Mining Application: Recognition of Health Problems from Gait Patterns of Elderly, IEEE International Conference on Data Mining Workshops, 2010

[15]B.L. Gunjal, R.R.Manthalkar, Discrete Wavelet Transform based Strongly Robust Watermarking Scheme for Information Hiding in Digital Images, Third International Conference on Emerging Trends in Engineering and Technology, 2010

[16]Turghunjan Abdukirim, Koichi Niijima, Shigeru Takano, Design Of Biorthogonal Wavelet Filters Using Dyadic Lifting Scheme, Bulletin of Informatics and Cybernetics Research Association of Statistical Sciences, Vol.37, 2005

[17]Seungsuk Ha, Youngjoon Han, Hernsoo Hahn, Adaptive Gait Pattern Generation of Biped Robot based on Human’s Gait Pattern Analysis, World Academy of Science, Engineering and Technology 34 2007

[18]Maodi Hu, Yunhong Wang, Zhaoxiang Zhang and Yiding Wang, Combining Spatial and Temporal Information for Gait Based Gender Classification, International Conference on Pattern Recognition 2010

[19]Xuelong Li, Stephen J. Maybank, Shuicheng Yan, Dacheng Tao, and Dong Xu, Gait Components and Their Application to Gender Recognition, IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications And Reviews, Vol. 38, No. 2, March 2008

[20]Shiqi Yu, , Tieniu Tan, Kaiqi Huang, Kui Jia, Xinyu Wu, A Study on Gait-Based Gender Classification, IEEE Transactions On Image Processing, Vol. 18, No. 8, August 2009

[21]M.Hanmandlu, R.Bhupesh Gupta, Farrukh Sayeed, A.Q.Ansari, An Experimental Study of different Features for Face Recognition, International Conference on Communication Systems and Network Technologies, 2011

[22]S. Handri, S. Nomura, K. Nakamura, Determination of Age and Gender Based on Features of Human Motion Using AdaBoost Algorithms, 2011

[23]Massimo Piccardi, Background Subtraction Techniques: Review, ~massimo/BackgroundSubtractionReview-Piccardi.pdf

[24]Bakshi, B., "Multiscale PCA with application to MSPC monitoring," AIChE J., 44, pp. 1596-1610., 1998

[25]G. Huang, Y. Wang, Gender Classification Based on Fusion of Multi-view Gait Sequences, Proceedings of the Asian Conference on Computer Vision, 2007.

[26]W. Kusakunniran et al., Multi-view Gait Recognition Based on Motion Regression Using Multilayer Perceptron, Proceedings of the IEEE International Conference on Pattern Recognition, pp 2186-2189, 2010

[27]Kohei Arai, Rosa Andrie, Human Gait Gender Classification in Spatial and Temporal Reasoning, International Journal of Advanced Research in Artificial Intelligence, Vol.1, No. 6, 2012

[28]L.Lee, WEL. Grimson, Gait Analysis for Recognition and Classification, Proceeding of the fifth IEEE International Conference on Automatic Face and Gesture Recognition, page 148-155, 2002.