Abstract
Gait exhibits several advantages with respect to other biometrics features: acquisition can be performed through cheap technology, at a distance and without people collaboration. In this paper we perform gait analysis using skeletal data provided by the Microsoft Kinect sensor. We defined a rich set of physical and behavioral features aiming at identifying the more relevant parameters for gait description. Using SVM we showed that a limited set of behavioral features related to the movements of head, elbows and knees is a very effective tool for gait characterization and people recognition. In particular, our experimental results shows that it is possible to achieve 96% classification accuracy when discriminating a group of 20 people.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ashbourn, J.: Biometrics - advanced identity verification: the complete guide. Springer (2002)
BenAbdelkader, C., Cutler, R., Davis, L.: View-invariant Estimation of Height and Stride for Gait Recognition. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, p. 155. Springer, Heidelberg (2002)
Cucchiara, R., Grana, C., Prati, A., Vezzani, R.: Computer vision system for in-house video surveillance. In IEE Proceedings of Vision, Image and Signal Processing (2005)
Goffredo, M., Bouchrika, I., Carter, J.N., Nixon, M.S.: Performance analysis for automated gait extraction and recognition in multi-camera surveillance. Multimedia Tools Appl. 50(1), 75–94 (2010)
Larsen, P.K.E., Simonsen, B., Lynnerup, N.: Gait analysis in forensic medicine. In Proc. Videometrics IX., vol. 6491 (January 2007)
Bouchrika, I., Goffredo, M., Carter, J., Nixon, M.: On using gait in forensic biometrics. Journal of Forensic Sciences 56(4), 882–889 (2011)
Winter, D.A.: The biomechanics and motor control of human gait: normal, elderly and pathological. University of Waterloo Press (1991)
Eastlack, M.E., Arvidson, J., Snyder-Mackler, L., Danoff, J.V., McGarvey, C.L.: Interrater reliability of videotaped observational gait-analysis assessments. Phys. Ther. 71(6), 465–472 (1991)
Kressig, R.W., Beauchet, O.: Guidelines for clinical applications of spatio-temporal gait analysis in older adults. Aging Clinical and Experimental Research, 18(2):174–176 (2006)
Brown, M., Sinacore, D.R., Binder, E.F., Kohrt, W.M.: Physical and performance measures for the identification of mild to moderate frailty. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 55(6), M350–M355 (2000)
Zhang, R., Vogler, C., Metaxas, D.: Human gait recognition. In: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2004) Volume 1 - Volume 01, CVPRW 2004, IEEE Computer Society. pp. 18–25, Washington, DC (2004)
Urtasun, R., Fua, P.: 3d tracking for gait characterization and recognition. In: Proc. IEEE Automatic Face and Gesture Recognition, Seoul, Korea, pp. 17–22 (2004)
Yoo, J.-H., Nixon, M.: Automated markerless analysis of human gait motion for recognition and classification. ETRI Journal 33(3), 259–266 (2011)
Wang, L., Tan, T., Ning, H.: Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1505–1518 (2003)
Preis, J., Kessel, M., Werner, M., Linnhoff-Popien, C.: Gait recognition with kinect. In: Proceedings of the First Workshop on Kinect in Pervasive Computing (2012)
Borràs, R., Lapedriza, À., Igual, L.: Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition. In: Campilho, A., Kamel, M. (eds.) ICIAR 2012, Part II. LNCS, vol. 7325, pp. 98–105. Springer, Heidelberg (2012)
Satta, R., Pala, F., Fumera, G., Roli, F.: Real-time appearance-based person re-identification over multiple kinect cameras. In: 8th International Conference on Computer Vision Theory and Applications (VISAPP 2013), Barcelona, Spain, 21/02/2013 (2013)
Ball, A., Rye, D., Ramos, F., Velonaki, M.: Unsupervised clustering of people from ‘skeleton’ data. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2012, ACM. pp. 225–226, New York (2012)
Gianaria, E., Balossino, N., Grangetto, M., Lucenteforte, M.: Gait characterization using dynamic skeleton acquisition. In: IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), pp. 440–445 (September 2013)
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT 1992, ACM. pp. 144–152, New York (1992)
Aizerman, M.A., Braverman, E.A., Rozonoer, L.: Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25, 821–837 (1964)
Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27 (2011)
Crisp, D.J., Burges, C.J.C.: A geometric interpretation of v-svm classifiers. In: Solla, S.A., Leen, T.K., Müller, K. (eds.) Advances in Neural Information Processing Systems 12, MIT Press. pp. 244–250 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Gianaria, E., Grangetto, M., Lucenteforte, M., Balossino, N. (2014). Human Classification Using Gait Features. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds) Biometric Authentication. BIOMET 2014. Lecture Notes in Computer Science(), vol 8897. Springer, Cham. https://doi.org/10.1007/978-3-319-13386-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-13386-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13385-0
Online ISBN: 978-3-319-13386-7
eBook Packages: Computer ScienceComputer Science (R0)