CFP last date
20 May 2024
Reseach Article

An Efficient Gait based Recognition using Bat Algorithm

by M. Aasha, S. Sivakumari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 9
Year of Publication: 2015
Authors: M. Aasha, S. Sivakumari
10.5120/ijca2015907548

M. Aasha, S. Sivakumari . An Efficient Gait based Recognition using Bat Algorithm. International Journal of Computer Applications. 132, 9 ( December 2015), 41-45. DOI=10.5120/ijca2015907548

@article{ 10.5120/ijca2015907548,
author = { M. Aasha, S. Sivakumari },
title = { An Efficient Gait based Recognition using Bat Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 9 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number9/23626-2015907548/ },
doi = { 10.5120/ijca2015907548 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:28:57.167996+05:30
%A M. Aasha
%A S. Sivakumari
%T An Efficient Gait based Recognition using Bat Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 9
%P 41-45
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Gait is the walking style of a person. The gait recognition method uses the concept of extracting the features from the video sequence. These features can be used in surveillance systems to identify the individual. In this paper, gait recognition using Multi objective Bat algorithm is proposed in which the shape descriptor features are included to improve the accuracy of gait recognition. Gait recognition of individuals is done by considering the shape features along with the best informative less effective part and most effective parts which are extracted from silhouettes by considering the effect of various cofactors. The shape of the movable parts of human body varies with motion and hence only the most informative movable parts with fixed movement are considered. The shape features can be extracted by angular radial transform and FFT is used for converting them from frequency domain. The results are evaluated using Multi objective PSO and Multiobjective Bat algorithm and it is observed that the proposed gait recognition using Bat algorithm achieves better results when compared to that of the PSO method.

References
  1. Zhaoxiang Zhang, Maodi Hu, and Yunhong Wang. "A survey of advances in biometric gait recognition." In Proc Biometric Recognition. Springer Berlin Heidelberg, pp 150-158, 2011
  2. Jin Wang, Mary She, Saeid Nahavandi, and Abbas Kouzani. "A review of vision-based gait recognition methods for human identification." In Proc IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 320-327, 2010.
  3. Davrondzhon Gafurov, Janne Hagen, and Einar Snekkenes. "Temporal characteristics of gait biometrics." In Proc IEEE Second International Conference on Computer Engineering and Applications (ICCEA), vol. 2, pp. 557-561, 2010.
  4. J.B. Dec. M. Saunders, V. T. Inman and H. D. Eberhart, “The Major Determinants in Normal and Pathological Gait”, The Journal of Bone and Joint Surgery, vol. 35,no. 3, pp. 543– 558, 1953.
  5. S. R. Das, M. T. Lazarewicz and L. H. Finkel, “Principal Component Analysis of Temporal and Spatial Information for Human Gait Recognition”, Proc. IEEE IEMBS, vol. 2, pp. 4568–4571, 2004.
  6. L. Wang, H. Ning, T. Tan and W. Hu, “Fusion of Static and Dynamic Body Biometrics for Gait Recognition”, Proc. 9th IEEE International Conference on Computer Vision (ICCV'03), vol. 2, pp. 1449-1454, 2003.
  7. L. Wang, H. Ning, T. Tan and W. Hu, “Fusion of Static and Dynamic Body Biometrics for Gait Recognition”, IEEE Transactions On Circuits and Systems for Video Technology, vol. 14, no. 2, pp. 149–158, 2004
  8. A. Veeraraghavan, A. K. Roy-Chowdhury and R. Chellappa, “Matching Shape Sequences in Video with Applications in Human Movement Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1896–1909,2005.
  9. R. D. Green and L. Guan, “Quantifying and Recognizing Human Movement Patterns from Monocular Video Images-Part II: Applications to Biometrics”, IEEE Transactions on Circuits Systems for Video Technology, vol. 14, no. 2, pp. 191–198, 2004.
  10. Ruben Vera-Rodriguez, John SD Mason, Julian Fierrez, and Javier Ortega-Garcia. "Comparative analysis and fusion of spatiotemporal information for footstep recognition." In Proc IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 35, no. 4, pp.823-834, 2013.
  11. Muhammad Shahzad Cheema, Abdalrahman weiwi, and Christian Bauckhage. "Gait recognition by learning distributed key poses." In Proc 19th IEEE International Conference on Image Processing (ICIP), pp. 1393-1396, 2012.
  12. AbhayKochhar, Deepika Gupta,Madasu Hanmandlu, and ShantaramVasikarla. "Silhouette based gait recognition based on the area features using both model free and model based approaches." In Proc IEEE International Conference on Technologies for Homeland Security (HST), pp. 547-551, 2013.
  13. BogdanPogorelc, ZoranBosnić, and Matjaž Gams. "Automatic recognition of gait-related health problems in the elderly using machine learning." In Proc Multimedia Tools and Applications, vol 58, no. 2, pp. 333-354, 2012.
  14. Chen Wang, Junping Zhang, Liang Wang, JianPu, and Xiaoru Yuan. "Human identification using temporal information preserving gait template." In Proc IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 34, no. 11,pp. 2164-2176, 2012.
  15. Rokanujjaman, M., M. Hossain, and M. Islam.2012. Effective Part Selection for Part-based Gait Identification, In: Proceedings of the 7th International Conference on Electrical and Computer Engineering. BUET, Dhaka, Bangladesh, December 20-22, pp. 17–19.
  16. Rokanujjaman, M., Hossain, N., Islam, M., Makihara.Y., and Yagi,Y. Effective Part-based Gait Identification using Frequency-domain Gait Entropy Features. Multimedia Tools and Application, 2015; 74(9): pp.2861-2877.
  17. Aasha.M, and S.Sivakumari. An Optimized Part based gait recognition using Multi Objective Particle Swarm Optimization. International Journal of Performability Engineering, Vol. 11, No. 5, September 2015, pp. 481-489.
  18. OnlineCASIADatabasehttp://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp
Index Terms

Computer Science
Information Sciences

Keywords

Gait recognition Multi-objective PSO BAT algorithm Shape feature.