Advertisement

Gender Identification Using Gait Biometrics

  • Richa ShuklaEmail author
  • Reenu Shukla
  • Anupam Shukla
  • Nirupama Tiwari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 243)

Abstract

Soft biometrics-based gender classification is an interesting and a challenging area of neural networking and has potential application in visual surveillance as well as human–computer interaction. In this paper, we have investigated gender recognition from human gait in image sequence. For the above purpose, we have extracted silhouette of 15 males and 15 females from the database collected from CASIA Gait Database (Dataset B). The computer-vision-based gender classification is then carried out on the basis of standard deviation, center of mass, and height from head to toe. Experimental results demonstrate that the present gender recognition systems achieve superior recognition performance of 96.8 % on feed-forward back-propagation (FFBP) network. Data on different networks have also been trained and tested. The above study indicates that gait-based gender recognition is one of the best reliable biometric technologies that can be used to monitor people without their cooperation. Controlled environments such as banks, military installations, and even airports need to quickly detect threats and provide differing levels of access to different user groups.

Keywords

Gender recognition Gait Silhouette Feature extraction Neural network 

References

  1. 1.
    Johansson, G.: Visual perception of biological motion and a model for its analysis. Percept. Psycho. Phys. 14(2), 201–211 (1973)CrossRefGoogle Scholar
  2. 2.
    Stevenage, S.V., Nixon, M.S., Vince, K.: Visual analysis of gait as a cue to identity. Appl. Cogn. Psych. 13, 513–526 (1999)CrossRefGoogle Scholar
  3. 3.
    Foster, J.P., Nixon, M.S., Prudel-Bennett, A.: Automatic gait recognition using area-based metrics. Pattern Recogn. Lett. 24, 2489–2497 (2003)CrossRefGoogle Scholar
  4. 4.
    Ioannidis, D., Tzovaras, D., Damousis Argyropoulos, S., Moustakas, K.: Gait recognition using compact feature extraction transforms and depth information. IEEE Trans. Inf. Forensics Secur. 2(3), 623–630 (2007)CrossRefGoogle Scholar
  5. 5.
    Golomb, B.A., Lawrence, D.T., Sejnowski, T.J.: SexNet: A neural network identifies sex from human faces. In: Proceedings of Internat. Conference on Advances in Neural Information Processing Systems, NIPS 3, vol. 3, pp. 572–577 (1991)Google Scholar
  6. 6.
    Harb, H., Chen, L.: Gender identification using a general audio classifier. In: Proceedings of International Conference on Multimedia and Expo, ICME’03, Washington, DC, USA, vol. 1(6), pp. 733–736 (2003)Google Scholar
  7. 7.
    Li, X., Maybank, S.J., Yan, S., Tao, D., Xu, D.: Gait components and their application to gender recognition. IEEE Trans. Syst. Man Cybern. C, Appl. Rev. 38(2), 145–155 (2008)CrossRefGoogle Scholar
  8. 8.
    Yazdanpanah, A.P., Fa, Z.K., Amirfattahi, R.: Multimodal biometric system using face, ear and gait biometrics. In: Proceedings of 10th International Conference on Information Science, Signal Processing and their Applications, ISSPA, vol. 10, pp. 251–254 (2010)Google Scholar
  9. 9.
    Zhang, D., Wang, Y.: Investigating the separability of features from different views for gait based gender classification. In: Proceedings of 19th International Conference on Pattern Recognition, ICPR, pp. 1–4 (2008)Google Scholar
  10. 10.
    Yu, S., Tan, T., Huang, K., Jia, K., Wu, X.: A study on gait-based gender classification. IEEE Trans. Image Proc. 18(8), 1905–1910 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kala, R., Shukla A., Tiwari, R.: Fuzzy Neuro systems for machine learning for large data sets. In: Proceedings of the IEEE International Advance Computing Conference, Patiala, India IACC ‘09, pp. 541–545 (2009)Google Scholar
  12. 12.
    Ahmed, J., Jafri, M.N., Ahmad, J., Khan, M.I.: Design and implementation of a neural network for real-time object tracking. In: Proceedings of 4th World Enformatika Conference Machine Vision and Pattern Recognition (2005)Google Scholar
  13. 13.
    Zhou, J., Sandhu P.S., Rani, S.: A neural based approach for modeling of severity of defects in function based software system. In: Proceedings of International Conference on Electronics and Information Engineering, ICEIE, vol. 2, pp. V2-568–V2-575 (2010)Google Scholar
  14. 14.
    Demuth, H., Beale, M., Hagan, M.: Neural Network Toolbox User’s Guide. The MathWorks, Inc., Natrick (2009)Google Scholar
  15. 15.

Copyright information

© Springer India 2014

Authors and Affiliations

  • Richa Shukla
    • 1
    Email author
  • Reenu Shukla
    • 2
  • Anupam Shukla
    • 3
  • Nirupama Tiwari
    • 1
  1. 1.Department of Computer ScienceSRCEM BanmoreMorenaIndia
  2. 2.Department of Computer Science Oriental UniversityIndoreIndia
  3. 3.Department of Computer Science ABV-Indian Institute of Information Technology and Management GwaliorGwaliorIndia

Personalised recommendations