Gait Feature Extraction and Recognition in Biometric System

Authors

  • Syed Nafis Syed Ngah Ismail School of Computer and Communication Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Perlis
  • Muhammad Imran Ahmad School of Computer and Communication Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Perlis
  • Said Amirul Anwar School of Computer and Communication Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Perlis
  • Mohd Nazrin Mohd Isa School of Microelectronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Perlis
  • Ruzelita Ngadiran School of Computer and Communication Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600, Perlis

Keywords:

Biometric, Gait Recognition, Information Fusion,

Abstract

This research focus on the development an automatic human identification system using gait sequence images. Human identification is widely used in computer vision applications such as surveillance system, criminal investigations and human-computer interaction. Gait sequence image is a nonstationary data and can be modelled using a statistical learning technique. The propose technique consists of three different stages. The pre-processing stage computes the average silhouette images to capture the important information and get a better representation for gait silhouette data. Then a principle component analysis (PCA) technique is applied on the average silhouette to extract the important gait features and reduce a dimension of gait data. A linear projection method used in this stage is able to reduce redundant features and remove noise data from the gait image. Furthermore, this approach will increase a discrimination power in the feature space when dealing with low frequency information. Low dimensional feature distribution in the feature space is assumed Gaussian, thus the Euclidean distance classifier can be used in the classification stage. The propose algorithm is a model-free based which uses gait silhouette features for the compact gait image representation and a linear feature reduction technique to remove redundant and noise information. The proposed algorithm has been tested using a benchmark CASIA datasets. The experimental results show that the best recognition rate is 90%

References

Kaur, P., Human Identification Using GAIT Recognition Technique with PAL and PAL Entropy and NN. International Journal of Computer Science and Information Technologies, 2014. 5(3): p. 3281 -3285.

Amit Kale, A.S., A. N. Rajagopalan, Naresh P. Cuntoor, Amit K. RoyChowdhury, Volker Krüger, and Rama Chellappa, Identification of Humans Using Gait. IEEE Transactions on Image Processing 2004. 13(9): p. 1163-1173.

Bobick, A.F., & Johnson, A. Y, Gait recognition using static, activityspecific parameters. Proceeding of the Computer Vision and Pattern Recognition, 2001.

Yam, C., Nixon, M. S., & Carter, J. N, Automated person recognition by walking and running via model-based approaches. Pattern Recognition, 2004. 37(5): p. 1057-1072.

Jin Wang, M.S., Saeid Nahavandi, Abbas Kouzani, A Review of Vision-based Gait Recognition Methods for Human

Identification. 2010 Digital Image Computing: Techniques and Applications, 2010.

N. V. Boulgouris, D.H., and K. N. Plataniotis, Gait recognition: a challenging signal processing technology for biometric identification. IEEE Signal Processing Magazine, 2005. 22: p. 78-90.

W. Liang, N.H., T. Tieniu, and H. Weiming, Fusion of static and dynamic body biometrics for gait recognition. IEEE Transactions on Circuits and Systems for Video Technology, 2004. 14: p. 149-158.

Chi, N.V.B.a.Z.X., Human gait recognition based on matching of body components. Pattern Recognition, 2007. 40: p. 1763-1770.

L. Xuelong, S.J.M., Y. Shuicheng, T. Dacheng, and X. Dong, Gait Components and Their Application to Gender Recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2008. 38: p. 145-155.

D. Cunado, M.N., and J. N. Carter, Automatic extraction and description of huan gait models for recognition purposes. Computer Vision and Image understanding, 2003. 90(1): p. 1-41.

Rong Zhang a, Christian Vogler b, Dimitris Metaxas a, Human gait recognition at sagittal plane. Image and Vision Computing, 2007. 25: p. 321–330.

S. L. Dockstader, M.J.B., A. M. Tekalp, Stochastic kinematic modeling and feature extraction for gait analysis. IEEE Transactions on Image Processing, 2003. 12(8): p. 962-976.

R.poppe, Vision-based human motion analysis: An overview.

Computer vision and Image Understanding, 2007. 108(1-2): p. 4-18.

Nagel, S.W.a.H.H., Tracking persons in Monocular Image Sequences. Computer vision and Image Understanding, 1999. 74(3): p. 174-192.

Hayder Ali, J.D., Chekima Ali, Ervin Gobin Moung, Gait Recognition using principle Component Analysis. Proceeding of the International Conference on Machine Vision 2010, 2010.

Shirke, S., Pawar, S., & Shah, K., Literature Review: Model Free Human Gait Recognition. Proceeding of the Communication Systems and Network Technologies (CSNT), 2014.

Dong Xu, Y.H., Zinan Zeng, Xinxing Xu, Human Gait Recognition Using Patch Distribution Feature and Locality-Constrained Group Sparse Representation. IEEE Transactions on Image Processing (T-IP), 2012. 21(1): p. 316-326.

Khalid Bashir, T.X., Shaogang Gong, Gait recognitionwithout subject cooperation. Pattern Recognition Letters, 2010. 31(13): p. 2052-2060.

W. Liang, T.T., H. Weiming, and N. Huazhong, Automatic gait recognition based on statistical shape analysis. IEEE Transactions on Image Processing, 2003. 12: p. 1120-1131.

Chi, N.V.B.a.Z.X., Gait Recognition Using Radon Transform and Linear Discriminant Analysis. IEEE Transactions on Image

Processing, 2007. 16: p. 731-740.

Sakai, H.M.a.R., Moving object recognition in eigenspace

representation: gait analysis and lip reading. Pattern recognition

Letters, 1996. 17(2): p. 155-162.

L. Wang, T.T., H. Ning, W. Hu, Silhouette analysis-based gait recognition for human identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003. 25: p. 1505-1518.

A. Kale, A.S., A. N. Rajagopalan, N. P. Cuntoor, et al., Identification of humans using gait. IEEE Transactions on Image Processing, 2004. 13: p. 1163-1173.

J. B. Hayfron-Acquah, M.S.N., J. N. Carter, Automatic gait recognition by symmetry analysis. Pattern Recognition Letters, 2003. 24(13): p. 2175-2183.

S. Sarkar, P.J.P., Z. Liu, I. R. Vega, et al., The humanID gait challenge problem: data sets, performance, and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005. 27: p. 162-177.

Davis, A.F.B.a.J.W., The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001. 23: p. 257-267.

Bhanu, J.H.a.B., Individual Recognition Using Gait Energy Image. IEEE Transations On Pattern Analysis And Machine Intelligence, 2006. 28(2): p. 316-322.

C. Chen, J.L., H. Zhao, H. Hu, and J. Tian, Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognition Letters, 2009. 30: p. 977-984.

Z. Xue, D.M., W. Song, B. Wan, and S. Jin, Infrared gait recognition based on wavelet transform and support vector machine. Pattern Recognition, 2010. 43: p. 2904-2910.

Toby H.W. Lam, K.H.C., James N.K. Liu, Gait flow image: a

silhouette-based gait representation for human identification. Pattern Recognition, 2011. 44: p. 973-987.

C. H. Chen, J.M.L., H. Zhao, et al., Factorial HMM and parallel HMM for gait recognition. Journal IEEE Transactions on Systems, Man and Cybernetics Part C Applications and Reviews, 2009. 39(1): p. 114-123.

Muhammad Rasyid Aqmar ⇑, Y.F., Yasushi Makihara, Yasushi Yagi, Gait recognition by fluctuations. Computer Vision and Image

Understanding, 2014. 126: p. 38-52.

Guoying Zhao, L.C., and Hua Li, Gait recognition using fractal scale. Pattern Analysis and Applications, 2007. 10: p. 235–246.

Downloads

Published

2016-07-01

How to Cite

Syed Ngah Ismail, S. N., Ahmad, M. I., Anwar, S. A., Mohd Isa, M. N., & Ngadiran, R. (2016). Gait Feature Extraction and Recognition in Biometric System. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(4), 127–132. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1187

Issue

Vol. 8 No. 4: Pioneering Future Innovation Through Green Technology II

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.