Abstract
In this paper, a topological approach for gait-based gender recognition is presented. First, a stack of human silhouettes, extracted by background subtraction and thresholding, were glued through their gravity centers, forming a 3D digital image I. Second, different filters (i.e. particular orders of the simplices) are applied on ∂ K(I) (a simplicial complex obtained from I) which capture relations among the parts of the human body when walking. Finally, a topological signature is extracted from the persistence diagram according to each filter. The measure cosine is used to give a similarity value between topological signatures. The novelty of the paper is a notion of robustness of the provided method (which is also valid for gait recognition). Three experiments are performed using all human-camera view angles provided in CASIA-B database. The first one evaluates the named topological signature obtaining 98.3% (lateral view) of correct classification rates, for gender identification. The second one shows results for different human-camera distances according to training and test (i.e. training with a human-camera distance and test with a different one). The third one shows that upper body is more discriminative than lower body.
Chapter PDF
Similar content being viewed by others
References
Golomb, B.A., Lawrence, D.T., Sejnowksi, T.J.: SEXNET: A neural network identifies sex from human faces. In: Lippmann, R.P., Moody, J.E., Touretzky, D.S. (eds.) Advances in Neural Information Processing Systems, vol. 3, pp. 572–579. Morgan Kaufmann Publishers, Inc. (1991)
Harb, H., Chen, L.: Gender identification using a general audio classifier. In: Proceedings of the 2003 International Conference on Multimedia and Expo, ICME 2003, vol. 1, pp. 733–736. IEEE (2003)
Yu, S., Tan, T., Huang, K., Jia, K., Wu, X.: A study on gait-based gender classification. IEEE Trans. Image Processing 18(8), 1905–1910 (2009)
Hu, M., Wang, Y., Zhang, Z., Zhang, D.: Gait-based gender classification using mixed conditional random field. IEEE Transactions on Systems, Man, and Cybernetics, Part B 41(5), 1429–1439 (2011)
Nixon, M.S., Carter, J.N.: Automatic recognition by gait. Proc. of IEEE 94(11), 2013–2024 (2006)
Kale, A., Sundaresan, A., Rajagopalan, A.N., Cuntoor, N.P., Chowdhury, A.K.R., Kruger, V., Chellappa, R.: Identification of humans using gait. IEEE Trans. Image Processing 13(9), 1163–1173 (2004)
Goffredo, M., Carter, J., Nixon, M.: Front-view gait recognition. In: Biometrics: Theory, Applications and Systems, September 29-October 1, pp. 1–6 (2008)
Aggarwal, J.K., Cai, Q.: Human motion analysis: A review. Computer Vision and Image Understanding 73(3), 428–440 (1999)
Mather, G., Murdoch, L.: Gender discrimination in biological motion displays based on dynamic cues. Proceedings of Biological Sciences 258(1353) (1994)
Lamar-León, J., García-Reyes, E.B., Gonzalez-Diaz, R.: Human gait identification using persistent homology. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 244–251. Springer, Heidelberg (2012)
Lamar, J., Garcia, E., Gonzalez-Diaz, R., Alonso, R.: An application for gait recognition using persistent homology. Electronic Journal Image-A 3(5) (2013)
Edelsbrunner, H., Harer, J.: Computational Topology - an Introduction. American Mathematical Society (2010)
Cohen-Steiner, D., Edelsbrunner, H., Harer, J.: Stability of persistence diagrams. Discrete & Computational Geometry 37(1), 103–120 (2007)
Hu, M., Wang, Y., Zhang, Z., Wang, Y.: Combining spatial and temporal information for gait based gender classification. In: 20th International Conference on Pattern Recognition (ICPR), pp. 3679–3682. IEEE (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Leon, J.L., Cerri, A., Reyes, E.G., Diaz, R.G. (2013). Gait-Based Gender Classification Using Persistent Homology. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41827-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-41827-3_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41826-6
Online ISBN: 978-3-642-41827-3
eBook Packages: Computer ScienceComputer Science (R0)