Cross-View Gait Recognition in 3-D Space Based on Gravity Center Track

Article Preview

Abstract:

We propose in this paper a novel cross-view gait recognition method based on gravity center trajectory (GCT). Inspired by the finding that if the GCT of human in walking process has regularity, the representation coefficients of the trajectory are generally consistent across different views. We propose to project the coefficients of GCT to different view plane (VP) which is the normal plane of view angle direction vector to achieve view-invariant features for gait recognition. Firstly, we obtain the GCT under different views by summation of pixel coordinates in body area. Then, we use the least square method to eliminate the upward or downward trend of GCT caused by view variance. Then, we project the GCT function to the corresponding VP. Lastly, we perform recognition by using a simple cluster method. Experimental results on the widely used CASIA-B gait database demonstrate the effectiveness and practicability of the proposed method.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

4210-4215

Citation:

Online since:

September 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] N.N. Liu Y.P. Tan, View Invariant Gait Recognition, in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2010, p.1410–1413.

DOI: 10.1109/icassp.2010.5495466

Google Scholar

[2] M. Goffredo et al., Self-calibrating view-invariant gait biometrics, IEEE Trans. Syst., Man, Cybern. B, vol. 40, no. 4, p.997–1008, (2010).

DOI: 10.1109/tsmcb.2009.2031091

Google Scholar

[3] W. Kusakunniran et al., Support vector regression for multi-view gait recognition based on local motion feature selection, in IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, 2010, p.974–981.

DOI: 10.1109/cvpr.2010.5540113

Google Scholar

[4] N. Liu, J. Lu, and Y.P. Tan, Joint Subspace Learning for View-Invariant Gait Recognition, IEEE Signal Processing Letters, vol. 18, pp.431-434, (2011).

DOI: 10.1109/lsp.2011.2157143

Google Scholar

[5] R. Bodor et al., View-independent human motion classification using image-based reconstruction, Image Vis. Comput., vol. 27, no. 8, p.1194–1206, (2009).

DOI: 10.1016/j.imavis.2008.11.008

Google Scholar

[6] I. Bouchrika and M.S. Nixon, Model-Based Feature Extraction for Gait Analysis and Recognition, Lecture Notes in Computer Science, vol. 4418, pp.150-160, (2007).

DOI: 10.1007/978-3-540-71457-6_14

Google Scholar

[7] X. Chen and T. Yang, Extraction Method of Gait Feature Based on Human Centroid Trajectory, Proceedings of the 2013 International Conference on Computer Engineering and Network (CENet2013), vol. 277, pp.515-523, (2014).

DOI: 10.1007/978-3-319-01766-2_59

Google Scholar

[8] W. Kusakunniran, Q. Wu, J. Zhang, H. Li , Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron, Pattern Recognition Letters, vol. 33, pp.882-889, (2012).

DOI: 10.1016/j.patrec.2011.04.014

Google Scholar

[9] G. Shakhnarovich, L. Lee, and T. Darrell, Integrated face and gait recognition from multiple views, in IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, 2001, p.439–446.

DOI: 10.1109/cvpr.2001.990508

Google Scholar

[10] W. Kusakunniran,Q. Wu, A New View-Invariant Feature for Cross-View Gait Recognition, IEEE Transactions on Information Forensics and Security, vol. 8, SI, pp.1642-1653, (2013).

DOI: 10.1109/tifs.2013.2252342

Google Scholar

[11] H.F. Hu, Enhanced Gabor Feature Based Classification Using a Regularized Locally Tensor Discriminant Model for Multiview Gait Recognition, IEEE Transcations on Circuits and Systerms for Video Technology, vol. 23, no. 7, pp.1274-1286, Jul (2013).

DOI: 10.1109/tcsvt.2013.2242640

Google Scholar

[12] M. Goffredo et al., Self-calibrating view-invariant gait biometrics, IEEE Trans. Syst., Man, Cybern. B, vol. 40, no. 4, p.997–1008, (2010).

DOI: 10.1109/tsmcb.2009.2031091

Google Scholar

[13] J. Lu and Y. Tan, Uncorrelated discriminant simplex analysis for view-invariant gait signal computing, Pattern Recognit. Lett., vol. 31, no. 5, p.382–393, (2010).

DOI: 10.1016/j.patrec.2009.11.006

Google Scholar