Abstract
Accurate gait recognition from video is a complex process involving heterogenous features, and is still being developed actively. This article introduces a novel framework, called GC2F, for effective and efficient gait recognition and classification. Adopting a ”refinement-and-classification” principle, the framework comprises two components: 1) a classifier to generate advanced probabilistic features from low level gait parameters; and 2) a hidden classifier layer (based on multilayer perceptron neural network) to model the statistical properties of different subject classes. To validate our framework, we have conducted comprehensive experiments with a large test collection, and observed significant improvements in identification accuracy relative to other state-of-the-art approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Liu, Z., Sarkar, S.: Simplest representation yet for gait recognition: Averaged silhouette. In: Proceedings of International Conference on Pattern Recognition, pp. 211–214 (2004)
Liu, Z., Sarkar, S.: Improved gait recognition by gait dynamics normalization. IEEE Trans. Pattern Anal. Mach. Intell. 28(6), 863–876 (2006)
Zhou, X., Bhanu, B.: Integrating Face and Gait for Human Recognition at a Distance in Video. IEEE Transactions on Systems, Man, and Cybernetics, Part B 37(5), 1119–1137 (2007)
Huang, P.: Automatic gait recognition via statistical approaches for extended template features. IEEE Transactions on Systems, Man, and Cybernetics, Part B 31(5), 818–824 (2001)
Liu, Z., Sarkar, S.: Effect of silhouette quality on hard problems in gait recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B 35(2), 170–183 (2005)
Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanid gait challenge problem: Data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162–177 (2005)
Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette Analysis-Based Gait Recognition for Human Identification. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1505–1518 (2003)
Wang, L., Ning, H., Tan, T., Hu, W.: Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans. Circuits Syst. Video Techn. 14(2), 149–158 (2004)
de Chazal, P., Flynn, J., Reilly, R.B.: Automated processing of shoeprint images based on the fourier transform for use in forensic science. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 341–350 (2005)
Lee, L., Grimson, W.E.L.: Gait analysis for recognition and classification. In: Proceedings of IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 148–155 (2002)
Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–30 (2004)
Ross, A., Dass, S.C., Jain, A.K.: Fingerprint warping using ridge curve correspondences. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 19–30 (2006)
Jain, A.K., Feng, J.: Latent palmprint matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(1) (2008)
BenAbdelkader, C., Cutler, R., Nanda, H., Davis, L.: Eigengait: Motion-based recognition of people using image self-similarity. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 284–294. Springer, Heidelberg (2001)
Cutting, J., Kozlowski, L.: Recognizing friends by their walk: Gait perception without familiarity cues. Bulletin Psychonomic Soc. 9(5), 353–356 (1977)
Murray, M., Drought, A., Kory, R.: Walking pattern of normal men. J. Bone and Joint Surgery 46-A(2), 335–360 (1964)
Wagg, D.K., Nixon, M.S.: On automated model-based extraction and analysis of gait. In: Proc. of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR 2004), pp. 11–16 (2004)
Guo, B., Nixon, M.S.: Gait feature subset selection by mutual information. IEEE Transactions on Systems, Man, and Cybernetics, Part A 39(1), 36–46 (2009)
Li, X., Maybank, S.J., Yan, S., Tao, D., Xu, D.: Gait components and their application to gender recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C 38(2), 145–155 (2008)
Niyogi, S., Adelson, E.: Analyzing and recognizing walking figures in xyt. In: Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 469–474 (1994)
Bregler, C.: Learning and recognizing human dynamics in video sequences. In: Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 568–574 (1997)
Bobick, A., Jonhson, A.: Gait recognition using static activity-specific analysis. In: Proc. of IEEE International Conference on Computer Vision and Pattern Recognition, CVPR (2001)
Dietterich, T., Bakiri, G.: Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research 2 (1995)
Cunado, D., Nixon, M., Carter, J.: On the learnability and design of output codes for multiclass problems. Comput. Vis. Image Understand. 90(1) (2003)
Müller, B., Reinhardt, J., Strickland, M.T.: Neural Networks: An Introduction (Physics of Neural Networks). Springer, Heidelberg (1995)
MacKay, D.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003)
McLachLan, G., Peel, D. (eds.): Finite Mixture Models. Wiley Interscience, Hoboken (2000)
Dempster, A., Laird, N., Rubin, D.: Likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1–38 (1977)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan Publishing, New York (1994)
Kong, E., Dietterich, T.: Error-correcting output coding corrects bias and variance. In: Proc. of the 12th IEEE International Conference on Machine Learning, pp. 313–321 (1995)
Crammer, K., Singer, Y.: On the learnability and design of output codes for multiclass problems. Machine Learning 47(2-3) (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shen, J., Pang, H., Tao, D., Li, X. (2010). Dual Phase Learning for Large Scale Video Gait Recognition. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-11301-7_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11300-0
Online ISBN: 978-3-642-11301-7
eBook Packages: Computer ScienceComputer Science (R0)