Article

Gender classification in human gait using support vector machine

Authors:

Mark S. NixonAuthors Info & Claims

ACIVS'05: Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems

Pages 138 - 145

https://doi.org/10.1007/11558484_18

Published: 20 September 2005 Publication History

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.

References

[1]

Dempster, W. T., and Gaughran, G. R. L.: Properties of Body Segments Based on Size and Weight. American Journal of Anatomy, 120 (1967) 33-54.

[2]

Foster, J. P., Nixon, M. S., and Prügel-Bennett, A.: Automatic Gait Recognition using Area-based Metrics. Pattern Recognition Letters, 24(14) (2003) 2489-2497.

Digital Library

[3]

Fukunaga K.: Introduction to Statistical Pattern Recognition. 2nd eds. Academic Press, San Diego (1990).

Digital Library

[4]

Goswami A.: A New Gait Parameterization Technique by Means of Cyclogram Moments: Application to Human Slop Walking. Gait and Posture, 8(1) (1998) 15-26.

[5]

Inman, V. T., Ralston, H. J., and Todd, F.: Human Walking. Williams & Wilkins, Baltimore (1981).

[6]

Johansson, G.: Visual Perception of Biological Motion and a Model for Its Analysis. Perception and Psychophysics, 14(2) (1973) 201-211.

[7]

Joachims, T.: Learning to Classify Text Using Support Vector Machines. Dissertation, Kluwer (2002).

Digital Library

[8]

Kozlowski, L. T., and Cutting, J. T.: Recognizing the Sex of a Walker from a Dynamic Point-Light Display. Perception and Psychology, 21(6) (1977) 575-580.

[9]

Mather, G., and Murdoch, L.: Gender Discrimination in Biological Motion Displays based on Dynamic Cues. In Proceedings of the Royal Society of London, Vol.B (1994) 273-279.

[10]

Murray, M. P., Drought, A. B., and Kory, R. C.: Walking Patterns of Normal Men. Journal of Bone and Joint Surgery, 46A(2) (1964) 335-360.

[11]

Nixon, M. S., Cater, J. N., Grant, M. G., Gordon, L., and Hayfron-Acquah, J. B.: Automatic Recognition by Gait: Progress and Prospects. Sensor Review, 23(4) (2003) 323-331.

[12]

Shutler, J. D., et al: On a Large Sequence-based Human Gait Database. In Proceedings of Recent Advances in Soft Computing, Nottingham, UK (2002) 66-71.

[13]

Shin, M., and Park, C.: A Radial Basis Function Approach to Pattern Recognition and Its Applications. ETRI Journal, 22(2) (2000) 1-10.

[14]

Whittle, M. W.: Gait Analysis: An Introduction. 3rd eds. Butterworth Heinemann (2002).

[15]

Winter, D. A.: The Biomechanics and Motor Control of Human Gait: Normal, Elderly and Pathological. Waterloo Biomechanics, Ontario (1991).

[16]

Yoo, J. H., and Nixon, M. S.: Markerless Human Gait Analysis via Image Sequences. In Proceedings of the ISB XIXth Congress, Dunedin, New Zealand (2003).

Cited By

Rasnayaka SSim T(2020)Your Tattletale Gait Privacy Invasiveness of IMU Gait Data2020 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB48548.2020.9304922(1-10)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1109/IJCB48548.2020.9304922
Hemmatpour MFerrero RMontrucchio BRebaudengo M(2019)A Review on Fall Prediction and Prevention System for Personal DevicesAdvances in Human-Computer Interaction10.1155/2019/96105672019Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1155/2019/9610567
Li ZXiong JYe X(2019)Gait Energy Image Based on Static Region Alignment for Pedestrian Gait RecognitionProceedings of the 3rd International Conference on Vision, Image and Signal Processing10.1145/3387168.3387201(1-6)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1145/3387168.3387201
Show More Cited By

Index Terms

Gender classification in human gait using support vector machine
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Video segmentation
      2. Computer vision tasks
        Scene understanding
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

Recommendations

Automatic recognition of gait patterns exhibiting patellofemoral pain syndrome using a support vector machine approach
Special section on computational intelligence in medical systems

Patellofemoral pain syndrome (PFPS) is a common disorder that afflicts people across all age groups, and results in various degrees of knee pain. The diagnosis of PFPS is difficult since the exact biomechanical factors and the extent to which they are ...
Gender Classification with Human Gait Based on Skeleton Model
ITNG '13: Proceedings of the 2013 10th International Conference on Information Technology: New Generations

Human gait walking skeleton model is proposed together with its implementation for gender classifications. The proposed model is based on morphological operations and is similar to the conventional skeleton model which allows calculations of joint ...
Termination of human gait
SMC'09: Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics

This study presents a detailed description of the events that take place during termination of human gait. This study was concerned with the patterns of foot-floor contact, ground reaction forces (Fy and Fz), electromyography (EMG) activities of the ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

ACIVS'05: Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems

September 2005

724 pages

ISBN:354029032X

Editors:
Jacques Blanc-Talon
DGA/D4S/MRIS, Arcueil, France
,
Wilfried Philips
Ghent University, Gent, Belgium
,
Dan Popescu
Wireless Technologies Lab, CSIRO ICT Centre, Marsfield, Australia
,
Paul Scheunders
Wireless Technologies Lab, University of Antwerp, Wilrijk, Belgium

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 20 September 2005

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 22 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Rasnayaka SSim T(2020)Your Tattletale Gait Privacy Invasiveness of IMU Gait Data2020 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB48548.2020.9304922(1-10)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1109/IJCB48548.2020.9304922
Hemmatpour MFerrero RMontrucchio BRebaudengo M(2019)A Review on Fall Prediction and Prevention System for Personal DevicesAdvances in Human-Computer Interaction10.1155/2019/96105672019Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1155/2019/9610567
Li ZXiong JYe X(2019)Gait Energy Image Based on Static Region Alignment for Pedestrian Gait RecognitionProceedings of the 3rd International Conference on Vision, Image and Signal Processing10.1145/3387168.3387201(1-6)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1145/3387168.3387201
Covre NNunnari FFornaser ADe Cecco M(2019)Generation of Action Recognition Training Data Through Rotoscoping and Augmentation of Synthetic AnimationsAugmented Reality, Virtual Reality, and Computer Graphics10.1007/978-3-030-25999-0_3(23-42)Online publication date: 24-Jun-2019
https://dl.acm.org/doi/10.1007/978-3-030-25999-0_3
Keyes O(2018)The Misgendering MachinesProceedings of the ACM on Human-Computer Interaction10.1145/32743572:CSCW(1-22)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1145/3274357
Li DSun BChen XGuo YWang H(2017)Gait Recognition of Lower Limb Rehabilitation Robot Based on Support Vector MachineProceedings of the 2017 International Conference on Artificial Intelligence, Automation and Control Technologies10.1145/3080845.3080874(1-5)Online publication date: 7-Apr-2017
https://dl.acm.org/doi/10.1145/3080845.3080874
Aasha MSivaranjani SSivakumari S(2016)An Effective reduction of Gait Recognition Time by using Gender ClassificationProceedings of the International Conference on Advances in Information Communication Technology & Computing10.1145/2979779.2979797(1-6)Online publication date: 12-Aug-2016
https://dl.acm.org/doi/10.1145/2979779.2979797
Das DChakrabarty A(2016)Human Gait Recognition using Deep Neural NetworksProceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies10.1145/2905055.2905192(1-6)Online publication date: 4-Mar-2016
https://dl.acm.org/doi/10.1145/2905055.2905192
Bruening DGoodyear CBowden DBarone R(2015)Gender recognition from biologically guided anthropometric featuresInternational Journal of Biometrics10.1504/IJBM.2015.0761377:4(354-372)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1504/IJBM.2015.076137
Collins MMiller PZhang J(2014)Gait Based Gender Recognition Using Sparse Spatio Temporal FeaturesProceedings of the 20th Anniversary International Conference on MultiMedia Modeling - Volume 832610.1007/978-3-319-04117-9_8(80-91)Online publication date: 6-Jan-2014
https://dl.acm.org/doi/10.1007/978-3-319-04117-9_8
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Table of Contents