Abstract
The gender of a person is easily recognized by his/her gait when training data and test data are from the same view. However, when it comes to cross-view gender classification, traditional methods can not deal with large view variation without enough labeled data in the target view. In this paper, we solve this problem by introducing a transfer learning based framework. Firstly, Gait Energy Image (GEI) of each gait sequence for all views is generated, and Principal Component Analysis (PCA) is carried out to obtain efficient gait representations. Subsequently, an inductive transfer learning approach, TrAdaBoost, is adopted to transfer knowledge from the source view to the target view, which significantly improves the performance of gait-based gender classification. Abundant experiments are conducted and experimental results demonstrate the superiority of the proposed method over traditional gait analysis methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Yu, S., Tan, T., Huang, K., Jia, K., Wu, X.: A study on gait-based gender classification. IEEE T-IP 18(8), 1905–1910 (2009)
Hu, M., Wang, Y., Zhang, Z., Wang, Y.: Combining spatial and temporal information for gait based gender classification. In: ICPR, pp. 3679–3682 (2010)
Bouchrika, I., Goffredo, M., Carter, J.N., Nixon, M.S.: Covariate analysis for view-point independent gait recognition. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 990–999. Springer, Heidelberg (2009)
Ben Abdelkader, C., Cutler, R., Davis, L.: Gait recognition using image self-similarity. EURASIP Journal on Advances in Signal Processing (4), 572–585 (2004)
Jean, F., Bergevin, R., Branzan, A.: Trajectories normalization for viewpoint invariant gait recognition. In: ICPR, pp. 1–4 (2008)
Kale, A., Chowdhury, A., Chellappa, R.: Towards a view invariant gait recognition algorithm. In: AVSBS, pp. 143–150 (2003)
Hu, M., Wang, Y., Zhang, Z., Zhang, D.: Multi-view multi-stance gait identification. In: ICIP, pp. 541–544 (2011)
Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: ICML, pp. 193–200 (2007)
Wu, P., Dietterich, T.G.: Improving svm accuracy by training on auxiliary data sources. In: ICML, pp. 871–878 (2004)
Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., Bowyer, K.: The humanID gait challenge problem: data sets, performance, and analysis. IEEE T-PAMI 27(2), 162–177 (2005)
Liu, Z., Sarkar, S.: Improved gait recognition by gait dynamics normalization. IEEE T-PAMI 28(6), 863–876 (2006)
Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE T-PAMI 28(2), 316–322 (2006)
Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)
Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: ICPR, pp. 441–444 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Yao, Z., Zhang, Z., Hu, M., Wang, Y. (2013). Cross-View Gait-Based Gender Classification by Transfer Learning. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-03731-8_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03730-1
Online ISBN: 978-3-319-03731-8
eBook Packages: Computer ScienceComputer Science (R0)