A GENDER RECOGNITION EXPERIMENT ON THE CASIA GAIT DATABASE DEALING WITH ITS IMBALANCED NATURE

Raúl Martín Félez, Ramón A. Mollineda, J. Salvador Sánchez

Abstract

The CASIA Gait Database is one of the most used benchmarks for gait analysis among the few non-small-size datasets available. It is composed of gait sequences of 124 subjects, which are unequally distributed, comprising 31 women and 93 men. This imbalanced situation could correspond to some real contexts where men are in the majority, for example, a sports stadium or a factory. Learning from imbalanced scenarios usually requires suitable methodologies and performance metrics capable of managing and explaining biased results. Nevertheless, most of the reported experiments using the CASIA Gait Database in gender recognition tasks limit their analysis to global results obtained from reduced subsets, thus avoiding having to deal with the original setting. This paper uses a methodology to gain an insight into the discriminative capacity of the whole CASIA Gait Database for gender recognition under its imbalanced condition. The classification results are expected to be more reliable than those reported in previous papers.

References

  1. Bauer, E. and Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach. Learning, 36(1-2):105-139.
  2. CASIA (2005). CASIA http://www.sinobiometrics.com.
  3. Davis, J. and Gao, H. (2004). Gender recognition from walking movements using adaptive three-mode PCA. In IEEE CVPR, Workshop on Articulated and Nonrigid Motion, volume 1.
  4. Huang, G. and Wang, Y. (2007). Gender classification based on fusion of multi-view gait sequences. In Proc. 8th Asian Conference Computer Vision, pages 462-471.
  5. Kang, P. and Cho, S. (2006). EUS SVMs: Ensemble of under-sampled SVMs for data imbalance problems. In ICONIP, pages 837-846.
  6. Lee, L. and Grimson, W. (2002). Gait analysis for recognition and classification. Proc. 5th IEEE Int'l. Conf. on Automatic Face and Gesture Recogn., pages 155-162.
  7. Li, X., Maybank, S., Yan, S., Tao, D., and Xu, D. (2008). Gait components and their application to gender recognition. IEEE Trans. SMC-C, 38(2):145-155.
  8. MIT (2001). Human Gait Recognition Database. MIT Artificial Intelligence Lab (Cambridge). http://www.ai.mit.edu/projects/gait/.
  9. Provost, F. and Fawcett, T. (1997). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. In Proc. of the 3rd ACM SIGKDD, pages 43-48.
  10. Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., and Bowyer, K. (2005). The HumanID gait challenge problem: data sets, performance, and analysis. IEEE Trans. on PAMI, 27(2):162-177.
  11. Shutler, J., Grant, M., Nixon, M. S., and Carter, J. N. (2002). On a large sequence-based human gait database. In Proc. 4th Int'l Conf. on RASC, pages 66-71.
  12. Yoo, J., Hwang, D., and Nixon, M. (2005). Gender classification in human gait using support vector machine. In Proc. ACIVS, pages 138-145.
  13. Yu, S., Tan, T., Huang, K., Jia, K., and Wu, X. (2009). A study on gait-based gender classification. IEEE Transactions on Image Processing, 18(8):1905-1910.
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Paper Citation


in Harvard Style

Martín Félez R., A. Mollineda R. and Salvador Sánchez J. (2010). A GENDER RECOGNITION EXPERIMENT ON THE CASIA GAIT DATABASE DEALING WITH ITS IMBALANCED NATURE . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 439-444. DOI: 10.5220/0002849204390444


in Bibtex Style

@conference{visapp10,
author={Raúl Martín Félez and Ramón A. Mollineda and J. Salvador Sánchez},
title={A GENDER RECOGNITION EXPERIMENT ON THE CASIA GAIT DATABASE DEALING WITH ITS IMBALANCED NATURE},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={439-444},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002849204390444},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - A GENDER RECOGNITION EXPERIMENT ON THE CASIA GAIT DATABASE DEALING WITH ITS IMBALANCED NATURE
SN - 978-989-674-029-0
AU - Martín Félez R.
AU - A. Mollineda R.
AU - Salvador Sánchez J.
PY - 2010
SP - 439
EP - 444
DO - 10.5220/0002849204390444