Das Choudhury, Sruti and Tjahjadi, Tardi. (2012) Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors. Pattern Recognition, Vol.45 (No.9). pp. 3414-3426. ISSN 0031-3203

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Abstract

This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH):	Gait in humans -- Mathematical models, Gait in humans -- Computer programs
Journal or Publication Title:	Pattern Recognition
Publisher:	Pergamon
ISSN:	0031-3203
Date:	September 2012
Volume:	Vol.45
Number:	No.9
Number of Pages:	13
Page Range:	pp. 3414-3426
Identification Number:	10.1016/j.patcog.2012.02.032
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	University of Warwick Postgraduate Research Scholarship
Version or Related Resource:	This items was also presented at the Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA'2011), Las Palmas de Gran Canaria, Spain, Jun 8-11, 2011.
References:	[1] J. Han, B. Bhanu, Individual recognition using gait energy image, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (2) (2006) 316-322. [2] L.Wang, H. Ning, T. Tan, Fusion of static and dynamic body biometrics for gait recognition, IEEE Transactions on Circuits and Systems for Video Technology, 14 (2) (2004) 149-158. [3] O. Barnich, M. V. Droogenbroeck, Frontal-view gait recognition by intra- and inter-frame rectangle size distribution, Pattern Recognition Letters, 30 (9) (2009), 893-901. [4] M. S. Nixon, T. N. Tan, and R. Chellappa, Human identification based on gait, Springer Science+Business Media, New York, USA, 2006. [5] F. Tafazzoli, R. Safabakhsh, Model-based human gait recognition using leg and arm movements, Engineering Applications of Artificial Intelligence, 23 (2010) 1237-1246. [6] W. Hu, T. Tan, L.Wang, S. Maybank, A survey on visual surveillance of object motion and behaviours, IEEE Transactions on Systems, Man, and Cybernetics, Part C 34 (3) (2004) 334-352. [7] M. Goffredo, I. Bouchrika, J. N. Carter, M. S. Nixon, Self-calibrating view-invariant gait biometrics, IEEE Transactions on Systems, Man, and Cybernetics, Part B 40 (4) (2010) 997-1008. [8] H. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, A full-body layered deformable model for automatic model-based gait recognition, EURASIP Journal on Advances in Signal Processing, 2008, 1-13. [9] S. L. Dockstader, M. J. Berg, A. M. Tekalp, Stochastic kinematic modeling and feature extraction for gait analysis, IEEE Transactions on Image Processing, 12 (8) (2003) 962-976. [10] L. Havasi, Z. Szlavik, T. Sziranyi, Higher order symmetry for non-linear classification of human walk detection, Pattern Recognition Letters, 27 (2006) 822-829. [11] Y. Ran, Q. Zheng, R. Chellappa, T. M. Stat, Applications of a simple characterization of human gait in surveillance, IEEE Transactions on Systems, Man, and Cybernetics, Part B 40 (4) (2010) 1009-1019. [12] N. V. Boulgouris, K. N. Plataniotis, D. Hatzinakos, Gait recognition using linear time normalization, Pattern Recognition, 39 (2006) 969-979. [13] S. Sarkar, P. J. Philips, Z. Liu, I. Vega, P. Grother, K. Bowyer, The HumanID gait challenge problem: data sets, performance, and analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (2) (2006) 162-177. [14] L.Wang, T. Tan, H. Ning, W. Hu, Silhouette analysis-based gait recognition for human identification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (12) (2003) 1505-1518. [15] J. D. Shutter, M. S. Nixon, C. J. Harris, Statistical gait description via temporal moments, in: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 291-295, 2000. [16] J. D. Shutter, M. S. Nixon, Zernike velocity moments for sequence-based description of moving features, Image and Vision Computing, 24 (2006) 343-356. [17] L.Wang, T. Tan, W. Hu, H. Ning, Automatic gait recognition based on statistical shape analysis, IEEE Transactions on Image Processing, 12 (9) (2003) 1-13. [18] C. BenAbdelkader, R. Culter, L. Davis, Stride and cadence as a biometric in automatic person identification and verification, in: Proceedings of the International Conference on Automatic Face and Gesture Recognition, 2002. [19] R. Collins, R. Gross, J. Shi, Silhouette-based human identification from body shape and gait, in: Proceedings of the International Conference on Automatic Face and Gesture Recognition, 2002. [20] A. Veraraghavan, A. K. Roy-Chowdhury, R. Chellappa, Matching shape sequences in video with applications in human movement analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (12) (2005) 1896-1909. [21] F. P. Kuhl, C. R. Giardina, Elliptic Fourier features of a closed contour, Computer graphics and image processing, 18 (1982) 236-258. [22] I. L. Dryden, and K. V. Mardia, Statistical shape analysis, 2nd ed. John Wiley & Sons LtD, England, 1998. [23] M. K. Hu, Pattern recognition by moment invariants, in: Proceedings of the Institute of Radio Engineers, vol. 49, no. 9, pp. 1428, 1961. [24] C. BenAbdelkader, R. G. Cutler, L. S. Davis, Gait recognition using image self-similarity, EURASIP Journal on Advances in Signal Processing, no. 4, pp. 572-585, 2004. [25] D. Tolliver, R. Collins, Gait shape estimation for identification, in: Proceedings of the International Conference on Audio- and Video-Based Biometric Person Authentication, 2003. [26] D. Ioannidis, D. Tzovaras, I. G. Damousis, S. Argyropoulos, K. Moustakas, Gait recognition using compact feature extraction transforms and depth information, IEEE Transactions on Information Forensics and security, 2 (3) (2007) 623-630. [27] K. Bashir, T. Xiang, S. Gong, Gait recognition without subject cooperation, Pattern Recognition Letters, 31 (2010) 2052-2060. [28] C. Wang, J. Zhang, J. Pu, X. Yuan, L. Wang, Chrono-Gait Image: A novel temporal template for gait recognition, European Conference on Computer Vision, Part 1, LNCS 6311, pp. 257-270, 2010. [29] Toby H. W. Lam, K. H. Cheung, James N. K. Liu, Gait flow image: A silhouette-based gait representation for human identification, Pattern Recognition, 44 (2011) 973-987. [30] M. A. Hossain, Y. Makihara, J. Wang, Y. Yagi, Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control, Pattern Recognition, 43 (2010) 2281-2291. [31] D. Tau, X. Li, X. Wu, S. J. Maybank, General tensor discriminant analysis and gabor features for gait recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (10) (2007) 1700-1715. [32] Z. Liu, S. Sarkar, Improved gait recognition by gait dynamics normalization, IEEE Transactions on Pattern Analysis andMachine Intelligence, 28 (6) (2006) 863-876.
URI:	http://wrap.warwick.ac.uk/id/eprint/44392

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