Abstract
Gait recognition is used to identify individuals by the way they walk. Recent research in automated human gait recognition has mainly focused on developing robust features representations and matching algorithms. To our best knowledge, feature selection is rarely addressed in gait classification problems. In this paper, we evaluate the performance of a particle swarm optimization (PSO) algorithm assisted by a support vector machine (SVM) for feature selection in gait classification. In this way, while PSO generates trial feature subsets, SVM estimates their fitness value during the search process. The resulting subset is evaluated by means of a SVM classifier to obtain the fitness value (correct classification rate) of such particle. The performance of the proposed approach is evaluated by using the well-known Southampton covariate database (SOTON). Our experimental results indicate that our proposed approach is able to achieve highly competitive results with respect to state-of-the-art approaches adopted in our comparative study.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bashir, K., Xiang, T., Gong, S., Mary, Q.: Gait representation using flow fields. In: BMVC, pp. 1–11 (2009)
Bouchrika, I., Nixon, M.S.: Exploratory factor analysis of gait recognition. In: 8th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2008, pp. 1–6. IEEE (2008)
Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3), 27 (2011)
Cortes, C., Vapnik, V.: Support-vector networks. Machine learning 20(3), 273–297 (1995)
Guo, B., Nixon, M.S.: Gait feature subset selection by mutual information. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 39(1), 36–46 (2009)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)
Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2), 316–322 (2006)
Johansson, G.: Visual perception of biological motion and a model for its analysis. Attention, Perception, & Psychophysics 14(2), 201–211 (1973)
John, G.H., Kohavi, R., Pfleger, K., et al.: Irrelevant features and the subset selection problem. In: Machine Learning: Proceedings of the Eleventh International Conference, pp. 121–129 (1994)
Kennedy, J.: Bare bones particle swarms. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003, pp. 80–87. IEEE (2003)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108. IEEE (1997)
Kennedy, J., Kennedy, J.F., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan Kaufmann (2001)
Khanesar, M.A., Teshnehlab, M., Shoorehdeli, M.A.: A novel binary particle swarm optimization. In: Mediterranean Conference on Control & Automation, MED 2007, pp. 1–6. IEEE (2007)
Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Neurocomputing, pp. 41–50. Springer (1990)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1), 273–324 (1997)
Lee, T.K., Belkhatir, M., Sanei, S.: A comprehensive review of past and present vision-based techniques for gait recognition. Multimedia Tools and Applications 72(3), 2833–2869 (2014)
Moraglio, A., Di Chio, C., Poli, R.: Geometric Particle Swarm Optimisation. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 125–136. Springer, Heidelberg (2007)
Pratheepan, Y., Condell, J.V., Prasad, G.: Individual Identification Using Gait Sequences under Different Covariate Factors. In: Fritz, M., Schiele, B., Piater, J.H. (eds.) ICVS 2009. LNCS, vol. 5815, pp. 84–93. Springer, Heidelberg (2009)
Shutler, J.D., Grant, M.G., Nixon, M.S., Carter, J.N.: On a large sequence-based human gait database. In: Lotfi, A., Garibaldi, J.M. (eds.) Applications and Science in Soft Computing. AISC, vol. 24, pp. 339–346. Springer, Heidelberg (2004)
Yeoh, T.-W., Tan, W.-H., Ng, H., Tong, H.-L., Ooi, C.-P.: Improved Gait Recognition with Automatic Body Joint Identification. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Shih, T.K., Velastin, S., Nyström, I. (eds.) IVIC 2011, Part I. LNCS, vol. 7066, pp. 245–256. Springer, Heidelberg (2011)
Yeoh, T., Zapotecas-Martinez, S., Akimoto, Y., Aguirre, H., Tanaka, K.: Genetic algorithm assisted by a svm for feature selection in gait classification. In: 2014 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 191–195. IEEE (2014)
Yoo, J.H., Nixon, M.S.: Automated markerless analysis of human gait motion for recognition and classification. Etri Journal 33(2), 259–266 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yeoh, T.W., Zapotecas-Martínez, S., Akimoto, Y., Aguirre, H.E., Tanaka, K. (2015). Feature Selection in Gait Classification Using Geometric PSO Assisted by SVM. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-23117-4_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23116-7
Online ISBN: 978-3-319-23117-4
eBook Packages: Computer ScienceComputer Science (R0)