Abstract
This work presents DGait, a new gait database acquired with a depth camera. This database contains videos from 53 subjects walking in different directions. The intent of this database is to provide a public set to explore whether the depth can be used as an additional information source for gait classification purposes. Each video is labelled according to subject, gender and age. Furthermore, for each subject and view point, we provide initial and final frames of an entire walk cycle. On the other hand, we perform gait-based gender classification experiments with DGait database, in order to illustrate the usefulness of depth information for this purpose. In our experiments, we extract 2D and 3D gait features based on shape descriptors, and compare the performance of these features for gender identification, using a Kernel SVM. The obtained results show that depth can be an information source of great relevance for gait classification problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Osu svm is a support vector machine (svm) toolbox for the matlab numerical environment, http://sourceforge.net/projects/svm/
Casia: Casia gait database (2005), www.cbsr.ia.ac.cn/english/GaitDatabases.asp
Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 316–322 (2006)
Kinect: Microsoft corp. redmond wa. kinect for xbox 360 (2010)
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: Applications and Reviews 38(2), 145–155 (2008)
Makihara, Y., Mannami, H., Yagi, Y.: Gait Analysis of Gender and Age Using a Large-Scale Multi-view Gait Database. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part II. LNCS, vol. 6493, pp. 440–451. Springer, Heidelberg (2011)
OpenNI: Openni organization, www.openni.org
Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape distributions. ACM Transactions on Graphics 21(4), 807–832 (2002)
Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., Bowyer, K.: The humanid gait challenge problem: data sets, performance, and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(2), 162–177 (2005)
Shan, C., Gong, S., McOwan, P.W.: Fusing gait and face cues for human gender recognition. Neurocomputing 71(10-12), 1931–1938 (2008)
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: Proceedings of the Computer Vision and Pattern Recognition Conference (2011)
Soton: University of southampton. database collection of programme automatic gait recognition for human id at a distance at soton
Tech., G.: Georgia tech. gvu center/college of computing. data of project human identification at a distance
Wang, Y.: Investigating the separability of features from different views for gait based gender classification. In: 19th International Conference on Pattern Recognition, ICPR 2008., pp. 1–4. IEEE (2008)
Yu, S., Tan, T., Huang, K., Jia, K., Wu, X.: A study on gait-based gender classification. IEEE Transactions on Image Processing 18(8), 1905–1910 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Borràs, R., Lapedriza, À., Igual, L. (2012). Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-31298-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31297-7
Online ISBN: 978-3-642-31298-4
eBook Packages: Computer ScienceComputer Science (R0)