EFFICIENT GAIT-BASED GENDER CLASSIFICATION THROUGH FEATURE SELECTION

Raúl Martín- Félez, Javier Ortells, Ramón A. Mollineda, J. Salvador Sánchez

2012

Abstract

Apart from human recognition, gait has lately become a promising biometric feature also useful for prediction of gender. One of the most popular methods to represent gait is the well-known Gait Energy Image (GEI), which conducts to a high-dimensional Euclidean space where many features are irrelevant. In this paper, the problem of selecting the most relevant GEI features for gender classification is addressed. In particular, an ANOVA-based algorithm is used to measure the discriminative power of each GEI pixel. Then, a binary mask is built from the few most significant pixels in order to project a given GEI onto a reduced feature pattern. Experiments over two large gait databases show that this method leads to similar recognition rates to those of using the complete GEI, but with a drastic dimensionality reduction. As a result, a much more efficient gender classification model regarding both computing time and storage requirements is obtained.

References

  1. Bashir, K., Xiang, T., and Gong, S. (2008). Feature selection on gait energy image for human identification. In Proc. IEEE Int'l Conf. on Acoustics, Speech and Signal Processing, ICASSP 2008, pages 985-988.
  2. Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proc. 5th Annual Workshop on Computational Learning Theory, pages 144-152.
  3. Brown, M. B. and Forsythe, A. B. (1974). The small sample behavior of some statistics which test the equality of several means. Technometrics, 16(1):129-132.
  4. CASIA (2005). CASIA Gait Database. http://www.sinobio metrics.com.
  5. Cutting, J. and Kozlowski, L. (1977). Recognizing friends by their walk: Gait perception without familiarity cues. Bulletin of the Psychonomic Society, 9:353-356.
  6. 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.
  7. Han, J. and Bhanu, B. (2006). Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2):316-322.
  8. Huang, G. and Wang, Y. (2007). Gender classification based on fusion of multi-view gait sequences. In Proc. 8th Asian Conf. Computer Vision, volume 1.
  9. Johansson, G. (1975). Visual motion perception. Scientific American, 6(232):76-80.
  10. Kozlowski, L. and Cutting, J. (1977). Recognizing the sex of a walker from a dynamic point-light display. Perception & Psychophysics, 21:575-580.
  11. 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.
  12. Makihara, Y., Mannami, H., and Yagi, Y. (2011). Gait analysis of gender and age using a large-scale multi-view gait database. LNCS. Computer Vision ACCV 2010, 6493:440-451.
  13. 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.
  14. 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.
  15. Yoo, J., Hwang, D., and Nixon, M. (2005). Gender classification in human gait using support vector machine. In Proc. ACIVS, pages 138-145.
  16. 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.
Download


Paper Citation


in Harvard Style

Martín- Félez R., Ortells J., A. Mollineda R. and Salvador Sánchez J. (2012). EFFICIENT GAIT-BASED GENDER CLASSIFICATION THROUGH FEATURE SELECTION . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-8425-99-7, pages 419-424. DOI: 10.5220/0003774404190424


in Bibtex Style

@conference{icpram12,
author={Raúl Martín- Félez and Javier Ortells and Ramón A. Mollineda and J. Salvador Sánchez},
title={EFFICIENT GAIT-BASED GENDER CLASSIFICATION THROUGH FEATURE SELECTION},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2012},
pages={419-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003774404190424},
isbn={978-989-8425-99-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - EFFICIENT GAIT-BASED GENDER CLASSIFICATION THROUGH FEATURE SELECTION
SN - 978-989-8425-99-7
AU - Martín- Félez R.
AU - Ortells J.
AU - A. Mollineda R.
AU - Salvador Sánchez J.
PY - 2012
SP - 419
EP - 424
DO - 10.5220/0003774404190424