Abstract
We describe an automated system that classifies gender by utilising a set of human gait data. The gender classification system consists of three stages: i) detection and extraction of the moving human body and its contour from image sequences; ii) extraction of human gait signature by the joint angles and body points; and iii) motion analysis and feature extraction for classifying gender in the gait patterns. A sequential set of 2D stick figures is used to represent the gait signature that is primitive data for the feature generation based on motion parameters. Then, an SVM classifier is used to classify gender in the gait patterns. In experiments, higher gender classification performances, which are 96% for 100 subjects, have been achieved on a considerably larger database.
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References
Dempster, W.T., Gaughran, G.R.L.: Properties of Body Segments Based on Size and Weight. American Journal of Anatomy 120, 33–54 (1967)
Foster, J.P., Nixon, M.S., Prügel-Bennett, A.: Automatic Gait Recognition using Area-based Metrics. Pattern Recognition Letters 24(14), 2489–2497 (2003)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, San Diego (1990)
Goswami, A.: A New Gait Parameterization Technique by Means of Cyclogram Moments: Application to Human Slop Walking. Gait and Posture 8(1), 15–26 (1998)
Inman, V.T., Ralston, H.J., Todd, F.: Human Walking. Williams & Wilkins, Baltimore (1981)
Johansson, G.: Visual Perception of Biological Motion and a Model for Its Analysis. Perception and Psychophysics 14(2), 201–211 (1973)
Joachims, T.: Learning to Classify Text Using Support Vector Machines. Dissertation. Kluwer, Dordrecht (2002)
Kozlowski, L.T., Cutting, J.T.: Recognizing the Sex of a Walker from a Dynamic Point-Light Display. Perception and Psychology 21(6), 575–580 (1977)
Mather, G., Murdoch, L.: Gender Discrimination in Biological Motion Displays based on Dynamic Cues. Proceedings of the Royal Society of London  B, 273–279 (1994)
Murray, M.P., Drought, A.B., Kory, R.C.: Walking Patterns of Normal Men. Journal of Bone and Joint Surgery 46A(2), 335–360 (1964)
Nixon, M.S., Cater, J.N., Grant, M.G., Gordon, L., Hayfron-Acquah, J.B.: Automatic Recognition by Gait: Progress and Prospects. Sensor Review 23(4), 323–331 (2003)
Shutler, J.D., et al.: On a Large Sequence-based Human Gait Database. In: Proceedings of Recent Advances in Soft Computing, Nottingham, UK, pp. 66–71 (2002)
Shin, M., Park, C.: A Radial Basis Function Approach to Pattern Recognition and Its Applications. ETRI Journal 22(2), 1–10 (2000)
Whittle, M.W.: Gait Analysis: An Introduction, 3rd edn. Butterworth-Heinemann, Butterworths (2002)
Winter, D.A.: The Biomechanics and Motor Control of Human Gait: Normal, Elderly and Pathological. Waterloo Biomechanics, Ontario (1991)
Yoo, J.H., Nixon, M.S.: Markerless Human Gait Analysis via Image Sequences. In: Proceedings of the ISB XIXth Congress, Dunedin, New Zealand (2003)
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© 2005 Springer-Verlag Berlin Heidelberg
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Yoo, JH., Hwang, D., Nixon, M.S. (2005). Gender Classification in Human Gait Using Support Vector Machine. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_18
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DOI: https://doi.org/10.1007/11558484_18
Publisher Name: Springer, Berlin, Heidelberg
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