Abstract
Any information about people such as their gender may be useful in some secure places; however, in some occasions, it is more appropriate to obtain such information in an unobtrusive manner such as using gait. In this study, we propose a novel method for gender classification using gait template, which is based on Radon Transform of Mean Gait Energy Image (RTMGEI). Robustness against image noises and reducing data dimensionality can be achieved by using Radon Transformation, as well as capturing variations of Mean Gait Energy Images (MGEIs) over their centers. Feature extraction is done by applying Zernike moments to RTMGEIs. Orthogonal property of Zernike moment basis functions guarantee the statistically independence of coefficients in extracted feature vectors. The obtained feature vectors are used to train a Support Vector Machine (SVM). Our method is evaluated on the CASIA database. The maximum Correct Classification Rate (CCR) of 98.94% was achieved for gender classification. Results show that our method outperforms the recently presented works due to its high performance.
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References
Sarah, V.S., Mark, S.N., Kate, V.: Visual analysis of gait as a cue to identity. Applied Cognitive Psychology 13(6), 513–526 (1999)
Li, X., Maybank, S.J., Yan, S., Tao, D., Xu, D.: Gait components and their application to gender recognition. IEEE Transactions on systems, Man, And Cybernetics-Part C 38(2) (2008)
Haihong, S., Liqun, M., Qishan, Z.: Gender categorization based on 3D faces. In: International Conference on Advanced Computer Control (ICACC), vol. 5, pp. 617–620 (2010)
Yoo, J.-H., Hwang, D., Nixon, M.S.: Gender classification in human gait using support vector machine. In: Blanc-Talon, J., Philips, W., Popescu, D.C., Scheunders, P. (eds.) ACIVS 2005. LNCS, vol. 3708, pp. 138–145. Springer, Heidelberg (2005)
Maodi, H., Yunhong, W.: A New Approach for Gender Classification Based on Gait Analysis. In: Fifth International Conference on Image and Graphics, pp. 869–874 (2009)
Lee, L., Grimson, W.E.L.: Gait Analysis for Recognition and Classification. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 148–155 (2002)
Sarker, S., jonathon Phillips, P., Liu, Z., Vega, I.R., Grother, P., Bouyer, K.W.: The Human ID Gait Challenge problem: data sets, performance and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(2) (February 2005)
Ju, H., Bir, B.: Individual Recognition UsingGait Energy Imag. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2) (2006)
Xiang-tao, C., Zhi-hui, F., Hui, W., Zhe-qing, L.: Automatic Gait Recognition Using Kernel Principal component Analysis. In: IEEE Int. Conference on Biomedical Engineering and Computer Science, Wuhan, pp. 1–4 (April 2010)
Ye, B., Peng, J.: Invariance analysis of improved Zernike moments. Journal of Optics A: Pure and Applied Optics 4(6), 606–614 (2002)
Ye, B., Peng, J.: Improvement and invariance analysis of Zernike moments using as a region-based shape descriptor. Journal of Pattern Recognition and Image Analysis 12(4), 419–428 (2002)
Chong, C.W., Raveendran, P., Mukundan, R.: Translation invariants of Zernike moments. Pattern Recognition 36(8), 765–773 (2003)
Maofu, L., Yanxiang, H., Bin, Y.: Image Zernike Moments Shape Feature Evaluation Based on Image Reconstruction. Geo-spatial Information Science 10(3), 191–195 (2007)
Maodi, H., Yunhong, W., Zhaoxiang, Z., Yiding, W.: Combining Spatial and Temporal Information for Gait Based Gender Classification. In: International Conference on Pattern Reconition (ICPR), Istanbul, pp. 3679–3682 (2010)
Huang, G., Wang, Y.: Gender classification based on fusion of multi-view gait sequences. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 462–471. Springer, Heidelberg (2007)
Yu, S., Tan, T., Huang, K., Jia, K.: X. Wu.: A study on gait-based gender classification. Image Processing Journal, IEEE T-IP 18(8), 1905–1910 (2009)
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Bagher Oskuie, F., Faez, K. (2011). Gender Classification Using a Novel Gait Template: Radon Transform of Mean Gait Energy Image. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_17
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DOI: https://doi.org/10.1007/978-3-642-21596-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21595-7
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